Computer Science and Information Systems

ComSIS is an international journal published by the ComSIS Consortium

ComSIS Consortium: University of Belgrade: Faculty of Organizational Science, Belgrade, Serbia Faculty of Mathematics, Belgrade, Serbia School of Electrical Engineering, Belgrade, Serbia Serbian Academy of Science and Art: Mathematical Institute, Belgrade, Serbia Union University: School of Computing, Belgrade, Serbia

University of Novi Sad: Faculty of Sciences, Novi Sad, Serbia Faculty of Technical Sciences, Novi Sad, Serbia Faculty of Economics, Subotica, Serbia Technical Faculty “Mihajlo Pupin”, Zrenjanin, Serbia University of Montenegro: Faculty of Economics, Podgorica, Montenegro

EDITORIAL BOARD: Editor-in-Chief: Mirjana Ivanović, University of Novi Sad Vice Editor-in-Chief: Ivan Luković, University of Novi Sad Managing Editors: Miloš Radovanović, University of Novi Sad Zoran Putnik, University of Novi Sad

Editorial Assistants: Vladimir Kurbalija, University of Novi Sad Jovana Vidaković, University of Novi Sad Ivan Pribela, University of Novi Sad Slavica Aleksić, University of Novi Sad Srđan Škrbić, University of Novi Sad Miloš Savić, University of Novi Sad

Editorial Board: S. Ambroszkiewicz, Polish Academy of Science, Poland P. Andreae, Victoria University, New Zealand Z. Arsovski, University of Kragujevac, Serbia D. Banković, University of Kragujevac, Serbia T. Bell, University of Canterbury, New Zealand D. Bojić, University of Belgrade, Serbia Z. Bosnić, University of Ljubljana, Slovenia B. Delibašić, University of Belgrade, Serbia I. Berković, University of Novi Sad, Serbia L. Böszörmenyi, University of Clagenfurt, Austria K. Bothe, Humboldt University of Berlin, Germany S. Bošnjak, University of Novi Sad, Serbia D. Letić, University of Novi Sad, Serbia Z. Budimac, University of Novi Sad, Serbia H.D. Burkhard, Humboldt University of Berlin, Germany B. Chandrasekaran, Ohio State University, USA V. Ćirić, University of Belgrade, Serbia G. Devedžić, University of Kragujevac, Serbia V. Devedžić, University of Belgrade, Serbia D. Đurić, University of Belgrade, Serbia D. Domazet, FIT, Belgrade, Serbia J. Đurković, University of Novi Sad, Serbia G. Eleftherakis, CITY College Thessaloniki, International Faculty of the University of Sheffield, Greece M. Gušev, FINKI, Skopje, FYR Macedonia S. Guttormsen Schar, ETH Zentrum, Switzerland P. Hansen, University of Montreal, Canada M. Ivković, University of Novi Sad, Serbia L.C. Jain, University of South Australia, Australia D. Janković, University of Niš, Serbia V. Jovanović, Georgia Southern University, USA Z. Jovanović, University of Belgrade, Serbia L. Kalinichenko, Russian Academy of Scence, Russia

Lj. Kašćelan, University of Montenegro, Montenegro Z. Konjović, University of Novi Sad, Serbia I. Koskosas, University of Western Macedonia, Greece W. Lamersdorf, University of Hamburg, Germany T.C. Lethbridge, University of Ottawa, Canada A. Lojpur, University of Montenegro, Montenegro M. Maleković, University of Zagreb, Croatia Y. Manolopoulos, Aristotle University, Greece A. Mishra, Atilim University, Turkey S. Misra, Atilim University, Turkey N. Mitić, University of Belgrade, Serbia A. Mitrović, University of Canterbury, New Zealand N. Mladenović, Serbian Academy of Science, Serbia S. Mrdalj, Eastern Michigan University, USA G. Nenadić, University of Manchester, UK D. Urošević, Serbian Academy of Science, Serbia A. Pakstas, London Metropolitan University, UK P. Pardalos, University of Florida, USA J. Protić, University of Belgrade, Serbia M. Racković, University of Novi Sad, Serbia B. Radulović, University of Novi Sad, Serbia D. Simpson, University of Brighton, UK M. Stanković, University of Niš, Serbia D. Starčević, University of Belgrade, Serbia D. Surla, University of Novi Sad, Serbia D. Tošić, University of Belgrade, Serbia J. Trninić, University of Novi Sad, Serbia M. Tuba, University of Belgrade, Serbia L. Šereš, University of Novi Sad, Serbia J. Woodcock, University of York, UK P. Zarate, IRIT-INPT, Toulouse, France K. Zdravkova, FINKI, Skopje, FYR Macedonia

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Computer Science and Information Systems

EDITORIAL

��

A True Random-Number Encryption Method Employing Block Cipher and PRNG

Abstract.

Keywords:

1. Introduction

2. Common Block Cipher

2.1. Data Encryption Standard (DES)

2.2. Advanced Encryption Standard (AES)

2.3 Block Cipher Mode of Operation

Pi

IV

Pi

IV

Pi

IV

Oi- KOi Oi Pi

Ci O IV

Pi Ci

3. The Proposed Method

3.1. The Parameters

File name

n

Filename_ext

SSC SSC SSC i i

i SSC i

hh

K hhs

KCTh

KCT h hh

KCT

WI WIWI

SdWI Sdth Sd

TRNSSdth

L L

y X X x xx X X x X y x X x i i X i X y

y X x X x X yX x X x X X

X

y X y X y X x x yy X x x y X x x

3.2. The Equations used to Generate Encryption Keys

h file namefilename_ext file name

filename_ext

DA KCT KCTKCT

DB DA DA KCT KCT DA

DADB

CDA DA SSC K h K h

CDB DB DA K h DA

CDA CDB

L DA DB Kh DA DB SSC

WI SSC DA SSC DB K h DA DB SSC

Sd SSC DA SSC DB K h DA DB SSC

Pk SSC DA SSC DA SSC DB SSC DBSSC DB DA

PRNS PRNS CTRNS LWI Sd Pk

strkE

strkE k s k s k s k s

nstr s s s s�

TRNS j SSC j TRNS E SSC j TRNS SSC j DB j

t DA DB DA DB TRNSTRNS

PRNS t h

Pk TRNS DA TRNS DB TRNS DA TRNS DB

strkE TRNS TRNS t Pk

3.3. The TRNEM Encryption Process

Step 1 h K hh

SSCsh K h

Step 2 DA DB KCT hCT K h KCT SSCs

DA DB

Step 3DA DB

SSCs K h CDA CDBCDA CDB DA DB

CDA CDB

Step 4 L WI Sd PkL

WI Sd Pk DA DB SSCs K h

Step 5 TRNS TRNS t PkL WI Sdth

TRNS

DA DB SSCs TRNS TRNS TRNSt Pk

SSC

Filename extension

File nameh

Step 1. Generating h

Step 2. Generating dynamic keys

Step 3. Encrypting dynamic keys

Step 4. Generating L,Web-Index, Start-distance,Pk

Step 5. Generating TRNS

Step 6-1. Generating PRNS1 and PRNS2

Step 6-2. Generating CTRNS

Step 6-3. Generating CTRNS

K h

Step 6. Generating PRNS1, PRNS2, CTRNS, and Ciphertext

SSC

K h

KCTDA

DB

SSC

K h

DA DBCDA

CDB

SSC

K h

DA DB

LWeb-IndexStart-distancePk

Web-Index

Start-distance

L

DA DBSSC

TRNS

TRNSTRNS

t

Pk

h

kP

KCT

PRNS1 ||PRNS2

t

PkCTRNS

TRNS

Plaintext

Pk

Fig. 1.

Step 6 PRNS PRNS CTRNS

Step 6-1 PRNS PRNSKCT KCT h t Pk

PRNS PRNS

Step 6-2 CTRNS CTRNSPk

Step 6-3Pk

Step 7 PRNS CDACDB CTRNS PRNS

PRNSCDA

CDB

PRNS

h

CTRNS

L t

Fig. 2.

3.4. The TRNEM Decryption Process

Step 1 h PRNSh

hPRNS K h

Step 2 DA DB CDA CDB

DA DB

DA CDA SSC K h K h SSC SSC SSC

DB CDB SSC DA DA K h SSC SSC

Step 3 L CTRNSSSCs K h DA DB

LCTRNS L

CTRNS L

CDA

CDB

PRNSCTRNS

L th K h

PRNSCTRNS

L tCDA

CDB

DA DB

CTRNS

L

TRNS PRNS

PRNSCDA

CDB

PRNS

h

CTRNS

L t

Fig. 3.

Step 4 TRNS TRNS CTRNS Pk

Pk DA DB SSCs

CTRNSPk TRNS

Step 5 t PRNSTRNS TRNS SSCs

TRNSt DA DB TRNS

PRNS

Step 6 PkTRNS TRNS DA DB

PkPk

3.5. The Features and Advantanges of the TRNEM

h hDA DB

DA DB

h L t

CDA CDB

PRNS PRNS

4. Security and Performance Analysis

h DA DB TRNSPk

4.1. Security of h

hSSC SSC

h

hh

h h

hh L t

DA DB L th

L t h

4.2. Security of Dynamic Keys DA and DB

DA DASSC SSC K h KCT

KCTDA CDA DA

SSC SSC K hCDA

h hDA DB CDB SSC

SSC K h DA DB

4.3. Security of the TRNS

WI

Sd L TRNS L

TRNSTRNS CTRNS

CTRNS h Lt

CTRNS

CTRNS TRNS PK TRNS

4.4. Security of a Wrapped Ciphertext File

PRNS h CTRNS L PRNS

t L th L t

h

4.5. Security of the Pk2

Pk

PkPk

Theorem 1.Pk n

Proof. Pk

PkPk TRNS DA TRNS DB TRNS DA TRNS DB

DA DB TRNS TRNS PkDA DB

TRNS TRNS TRNS DBDA DB

KCTTRNS TRNS TRNS

Pk

PRNSPRNS

Pk

PkPk

Pkn

4.6. Security of the TRNEM

4.7. Generation Times of Parameters

PRNS PRNS CDA CDB CTRNS

Table 1.

hDADBCDACDB

LWISdPk

TRNSTRNSTRNSTRNS

tPk

Table 2.

PRNS PRNS h tCDACDBCTRNS L

PRNS PRNS CDA CDB CTRNS

PRNS PRNS h t CTRNS L

DA DB

CDA CDB CDA CDBTRNS CTRNS

CTRNS

h L Pk TRNS TRNS TRNSTRNS t Pk

CTRNS

Table 3.

hCDA CDACDB CDB

DADBL

PkTRNSTRNSTRNSTRNS

tPk

Table 4. CTRNS

TRNS

Table 5.

hDA

DB

CDACDB

LWISdPk

TRNSE k str E k str

TRNSE k str E k str

TRNSE k str E k str

TRNSE k str E k str

tPk

DA

DB

CTRNS TRNS

4.8. Performance Analysis

Table 6.

E k str

CTRNS TRNS

E k str

TRNSCTRNS

PK

PK

KCTsDA DB Pk TRNS TRNS PK

KCTDA DB Pk TRNS TRNS

PKKCT TRNS

PRNS CDA CDB CTRNS PRNSh CDA CDB L t

ciphertexth CDA CDB L t ciphertext� � � � ��

5. Conclusions and Future Work

TRNS

h

K h DA DB PK PK2 SSCs

Acknowledgments.

References

Lectures

Yi-Li Huang

Fang-Yie Leu

Jiang-Hong Chen

William C. Chu

Received: September 21, 2013; Accepted: January 21, 2014.

A Secure Mobile DRM System Based on Cloud Architecture

Abstract.

Keywords:

1. Introduction

1.1 Cloud Architecture

��

1.2 Cloud Services Model

Fig. 1.

2. The DRM Related Work

��

2.1 Discussion of Using a Mobile Device to Implement DRM

��

3. Proposed Authentication Protocol

Fig. 2.

�

IDPWIMEI

su NN

SK

reqbookE �

reqMRESP

mESK SK mmDSK SK m

BA

h

3.1 Registration Phase

ID pw

3.2 Authentication Phase

Step 1 ID pw uN C C

ID IMEI C CStep 2 ID ID pw

uN �

IMEIwphhCNu ��

CIMEINhwphIDh u ��

sN

IMEINNhSK su ��

su NNIMEIpwhh � su NNIMEIh

Step 3 sN � sN �

suus NNIMEIpwhhNIMEIpwhhN ��

susu NNIMEIhNNh(IMEI �

uNIMEIpwhhC ��

IMEINhpwhIDhC u ��

Fig. 3.

SK

IMEINNhSK su ��

3.3 Service Response Phase

Step 1

reqSK bookEEC ��

reqSK MEC ��

�uN

�� uNIMEIpwhhC

IMEINhpwhIDhC u ��

CCCIDStep 2 ID SK

CDbookE SKreq ��

CDM SKreq ��

��uN

IMEIwphhCNu ��

CIMEINhwphIDh u ��

�sN

newSK

IMEINNhSK sunew ��

C

RESPECn ewSK�

C� �� su NNIMEIpwhh �� su NNIMEIh

Fig. 4.

Step 3 ��sN

�� suus NNIMEIpwhhNIMEIpwhhN

��sN

��susu NNIMEIhNNh(IMEI

�uN

newSK

IMEINNhSK sunew ��

C RESP

CDRESPn ewSK�

RESP

4. Security Analysis

4.1 DOS Attack Prevention

4.2 Password Guessing Attack Prevention

4.3 Insider Attack Prevention

pw xxpwhh �

4.4 Reply Attack Prevention

uN sN

uNIMEIpwhhC �� su NNIMEIpwhh �

�� uNIMEIpwhhC

�� su NNIMEIpwhh

4.5 Impersonation Attack Prevention

ID IMEIpw uNIMEIpwhhC ��

IMEIwphhCNu ��

4.6 Man-in-the-Middle Attack Prevention

XN pwh

uNIMEIpwhhC ��

su NNIMEIpwhh �

CIMEINhwphIDh u �� susu NNIMEIhNNIMEIh �

4.7 Parallel Sessions Attack Prevention

CCCC

su NNIMEIpwhh � su NNIMEIh

4.8 Session Key Error or Tampering

4.9 Comparison

Fig. 5.

5. Conclusions

References

Chin-Ling Chen

Woei-Jiunn Tsaur

Yu-Yi Chen

Yao-Chang Chung


N

Exchange of in home network:

Movement detection time:

Entering the visited network:

Confirmation of MN in visited network:

Interchange of data frames in the visited network:

Returning to the base network:

Movement between visited networks:

Received: September 17, 2013; Accepted: February 15, 2014.

Long Distance Face Recognition for Enhanced Performance of Internet of Things Service Interface

Abstract.

Keywords:

1. Introduction

2. Background and Related Work

2.1. Concept of Internet of Things for Service Interface

2.2. Algorithms of Face Recognition

Table 1.

1. P� PX x x x

2. i

i i

PT

i iix X x X

S x mean x mean mean xP

� �

� � � �� 3. Sw

C

W ii

S S�

��4. Sb

C PT i

B i i ii i

S n mean mean mean mean mean xP

� �

� � � �� 5. Sw Sb

TB

opt mTW

B i i W i i m

W S WW w w w

W S W

S w S w� �

� � � �

� �

�

C ni

Table 2.

1. PPY y y y�

2. P

i i i

i

y y mean mean yP

�

� � � �3. Wopt�i i

opty W y�

2.3. Interpolations for Image Normalization

IA B C

D

Fig. 1.

P0, P1, P2 P3

Fig. 2.

3. Proposed Long Distance Face Recognition System

Fig. 3.

WpcaWpca Wlda

Wlda

Fig. 4.

Fig. 5.

4. Experiment Result

Table 3.

��

�

��

Table 4.

4.1. Face Recognition Rate Changes according to Interpolations

Fig. 6.

Fig. 7.

Fig. 8.

Fig. 9.

4.2. Face Recognition Rate Change according to the Configuration of Training Images

Fig. 10.

4.3. Face Recognition Rate Change according to Similarity Measure

5. Conclusion

Acknowledgments.

References

Hae-Min Moon

Sung Bum Pan

.


PPS: A Privacy-Preserving Security Scheme for Multi-operator Wireless Mesh Networks with Enhanced User

Experience

Abstract.

Keywords:

1. Introduction

Fig.1

�

�

�

2. The Cryptographic Background

2.1. Bilinear Pairing

Te G G G� �

q

GT

T

abP Q G a b Z e a P b Q e b P a Q e P Qe P Q I

P Q G e P Q G

� � � � � � � �

�

� �

2.2. Short Signature (BLS)

Setup.G G

e G G G P G H G� � � �Key Generation.

qZx R x P R x� � �Sign.

V x H m V� �Verify.

V e R H m e P V��

2.3. Identity-based Proxy Signature

Setup.A A

B B

s Pand s s P PARA G G e P H H

��

qH G and H G Z are defined� � �

Delegation.

B A B B A B

B B

Cert s P where P H PK PKSKP Cert

� � �

� B Bs P�Sign.

q

B B

m r ZU V U r P h H m U V r h SKP

��

Verify. A B Be PK PK U h P e P V where

h H m U

� � ��

�

3. System Model

3.1. Definitions and Notations

Definitions.

Inter-operator handover.

Intra-operator handover.

Certificate.

Pseudonym.

Ticket.

Double deposit.

Notations.

Table 1.

��

3.2. Network Architecture

Fig.2

3.3. Trust Model

�

�

�

�

��

Fig.3

3.4. Privacy Model

Anonymity.

Untraceability.

Sophisticated unlinkability.

Accountability.

3.5. System Initialization

(1)q q

G G e P Q H H H P Q Ge H G Z H G H G Z� � � �

(2)B Bq

B B

SK S Z PKS P PARA G G e P Q H H H PK

� � ��

(1)O O O OqSK S Z PK S P

PARA� � � �

(2) � OO B PK�(3) B O O B OCert O S P where P H PK PK Exp� � �

(4) � O Cert O�

(5) O O O B O OSKP Cert O S P S S P� � �

(1) R R R RqSK S Z PK S PPARA

� � � �

(2) � RPK�

(3)O

O R R O R

B O R B O R IBPS Sign SKP

Cert R S P where P H PK PKExp RTK R Exp PK PK PK Exp PK PK PK� �

� � ��

(4) � Cert R RTK R�

(5) R R R O R RSKP Cert R S P S S P� � � �

4. PPS: The Proposed Scheme

4.1. Pseudonym Generation

Fig. 4

(1) M M M MqSK S Z PK S PPARA

� � � �

(2) � MPK�

(3) O M O M M O M M M M M O M

M M M M M

K e S Q PK k H K U k H ID A S UA ID A k U

� � � � � � �

(4) � �O M O MPK A S H ID� �� (5) M O M O M M OK e S Q PK k H K� � �

(6) � O MB ID U�

(7) M B MU S U� ��

(8) MU� ��

(9) M M B M M M M

M B O M M M M B O M

U k S H ID PS S H IDPK S S H ID SK S PK S S PS

��

��

O MS SO-M O M M O M-OK =e S Q PK e Q P e S Q PK K��

KO-M/KM-O kM

mMSK

��Mm SK H m� � ��

�

4.2. Ticket Insurance

(1) qr Z R r P� � �

(2) � INFO INFO B OR O O PK PK�

(3) qa b ZExp

� � �

(4) O O M O B O O

O B O INFO

d PK d PK d A P H PK PK t e R a P PKe b P PK PK C H d d d O t b

� � �

� � � � � � � � � ��

(5) � C�

(6) � � �O OS C SKP r PK(7) � S�

(8)O O B O

INFO

INFO

S S a PK C C b t e S P e C P PK PKC H d d d O t d d

O

� � � � � � � � � ��

INFOd d O

4.3. Inter-operator Authentication

(1) � �� B O R B O RExp PK PK PK 1 Exp PK PK PK�

(2)B O

M R M R

ExpPK PK 1

K e SK PK� �

��

��

(3)M

M INFO B O

BLS Sign SK

PS d d1 2 Exp O1 PK PK S C t1 2t1 t1

��

(4) B O M

Exp t2 PK PK PSS C

� ��

R M R M B OK e S PS PK PK��

(5) � e t e t e t��

(6) M RK

u H

� ��

� � CTK M e d v u� �� (7) u u v t t ��

(8)

R M

O B O

O

Kt e S P e C P PK PK

u H CTK M e v PK u d

� ��

�� O

INFO

C H d d v PK u dExp O t

��

�

(9) M INFO O M INFO

INFO O R

OTK M PS R PK Exp t PS R Exp tR PK PK t

��

(10)B O RPK PK PK� �

��

4.4. Intra-operator Authentication

(1) B O R B O RExp PK PK PK Exp PK PK PK��

(2)B O M R

M R

PK PK Ke SK PK

� � ��

(3)M R

M O INFO M O INFO

HMAC Sign K

PS PK R Exp t PS PK R Exp tt t t

��

� ��

(4)O R

R M R M B O

Exp tPK PK

K e S PS PK PK� �

�

�� R MK� ��

�

(5) � t t t��

(6)M RK� �

��

4.5. MC-MC Authentication

(1) � � ��

��

(2) B O M

ExpPK PK PS

S C

��

M MK e Q�

��

��

(3) � � ��

��

(4)

��

�

��

��

��

4.6. User Accountability

(1) � �

(2)�

� � ��

��

(3) �

(4)� �

5. System Analysis

5.1. Security and privacy analysis

Authenticity.

Confidentiality.

Anonymity.

MMM IDHSPS �� CSOdddCTK INFO

�� ROB PKPKPKERTK

Untraceability.

Sophisticated unlinkability.

User accountability.

5.2. Performance Analysis

Communication Overhead.

Exp TS

Inter-operator Communication Overhead.

Intra-operator Communication Overhead.

Table 2.

Scheme Inter-operator communication overhead Total bitsSAT

Total: 9|G1|+2|G2|+|HMAC|PPS

Total: 8|G1|+|HMAC|Note:

Table 3.

Scheme Intra-operator communication overhead Total bitsSAT

Total: 8|G1|+2|G2|+|HMAC|PPS

Total: 6|G1|+|HMAC|

Table 4.

SM PA BP MG MTP HashBLSsBLSvHIBSsHIBSvIBPSsIBPSvHMACsHMACvKA

Note:

Computation Overhead.

Inter-operator Computation Overhead.

IRCOSAT=4BP+2MTP+1SM+1PA+2MG+1Hash

IRCOPPS=3BP+1MTP+2SM+2PA+3Hash

IRCOSAT IRCOPPS

Table 5.

Scheme BLSs HIBSs HIBSv IBPSv HMACs HMACv KASATPPS

tBP =2 tMTP =3tSM

IRCOPPS IRCOSAT

Intra-operator Computation Overhead.

IACOSAT=4BP+2MTP+1SM+2MG+1Hash

IACOPPS=3BP+1SM+2PA+3Hash

IACOSAT IACOPPS

Table 6.

Scheme BLSs HIBSs HIBSv IBPSv HMACs HMACv KASATPPS

IACOPPS IACOSAT

6. Related work

7. Conclusion

Acknowledgements.

References

Tianhan Gao

Nan Guo

Kangbin Yim

Qianyi Wang


Computer Science and Information Systems 11(3):1001–1016 DOI: 10.2298/CSIS130915061J

A Computer Remote Control System Based on SpeechRecognition Technologies of Mobile Devices and Wireless

Communication Technologies

Hae-Duck J. Jeong, Sang-Kug Ye, Jiyoung Lim, Ilsun You, and WooSeok Hyun

Department of Computer SoftwareKorean Bible University

Seoul, South [email protected], [email protected]

{jylim, isyou, wshyun}@bible.ac.kr

Abstract. This paper presents a computer remote control system using speechrecognition technologies of mobile devices and wireless communication technolo-gies for the blind and physically disabled population as assistive technology. Thesepeople experience difficulty and inconvenience using computers through a keyboardand/or mouse. The purpose of this system is to provide a way that the blind andphysically disabled population can easily control many functions of a computervia speech. The configuration of the system consists of a mobile device such as asmartphone, a PC server, and a Google server that are connected to each other. Userscan command a mobile device to do something via speech; such as writing emails,checking the weather forecast, or managing a schedule. These commands are thenimmediately executed. The proposed system also provides blind people with a func-tion via TTS(Text To Speech) of the Google server if they want to receive contentsof a document stored in a computer.

Keywords: speech recognition technology, mobile device, Android, wireless com-munication technique.

1. Introduction

Speech recognition technology, which is able to recognize human speech and change totext, or to perform a command, has emerged as the ’Next Big Thing’ of the IT indus-try. Speech recognition is technology that uses desired equipment and a service whichcan be controlled through voice without using items such as a mouse or keyboard. Italso appeared as part of ongoing research in progress in 1950s, but was not popularizeduntil the mid-2000s, with low voice recognition. Presently, related speech recognitiontechnologies, which have been previously used limitedly for special-purposes, have beenrapidly evolving because of the proliferation of portable computing terminals such assmartphones interconnected with the expansion of the cloud infrastructure [8].

One of the most prominent examples of a mobile voice interface is Siri, the voice-activated personal assistant that comes built into the latest iPhone. But voice functionalityis also built into Android, the Windows Phone platform, and most other mobile systems,as well as many applications. While these interfaces still have considerable limitations,we are inching closer to machine interfaces we can actually talk to [7].

1002 Hae-Duck J. Jeong et al.

This paper presents a computer remote control system using speech recognition tech-nologies of mobile devices and wireless communication technologies for the blind andphysically disabled population [5], [6], [13]. These people experience difficulty and in-convenience using computers through a keyboard and/or mouse. The purpose of this sys-tem is to provide a way the blind and physically disabled population can easily controlmany functions of a computer via speech. The configuration of the system consists of amobile device such as a smartphone, a PC server, and a Google server that are connectedto each other. Users command a mobile device to do something via speech such as directlycontrolling computers, writing emails and documents, calculating numbers, checking theweather forecast, or managing a schedule. These commands are then immediately exe-cuted. The proposed system also provides blind people with a function via TTS (Text ToSpeech) of the Google server when they want to receive contents of a document stored ina computer.

In Section 2, a few related works and technologies of the proposed remote computercontrol system are discussed. Section 3 describes comparison of speech recognition ratesof current speech recognition systems. Section 4 presents how the proposed system usingspeech recognition technologies is designed and implemented, and finally the conclusionsare described in Section 5.

2. Related Works and Technologies

Related works and technologies of the proposed computer remote control system usingspeech recognition technologies of mobile devices and wireless communication technolo-gies are Android, and speech recognition algorithms as follows.

2.1. Android

Android is a Linux-based open mobile platform for mobile devices such as smartphonesand tablet computers. It is composed of not only an operating system, but also middle-ware, user interface (UI), browser, and application. It also includes C/C++ libraries thatare used in components of various Android systems [3]. Figure 1 shows that Androidsystem architecture is divided into five hierarchical categories: applications, applicationframework, libraries, Android runtime, and Linux kernel [1], [2], [9]. The proposed ap-plication was designed and developed on Android.

2.2. Speech Recognition Algorithms

Google Speech Recognition. Google uses artificial intelligence algorithms to recognizespoken sentences, stores voice data anonymously for analysis purposes, and cross matchesspoken data with written queries on the server. Key problems of computational power, dataavailability and managing large amounts of information are handled with ease usingandroid.speech.RecognizerIntent package [1]. Client application starts upand prompts user to input using Google Speech Recognition. Input data is sent to theGoogle server for processing and text is returned to client. Input text is passed to the nat-ural language processing (NLP) server for processing using HTTP (HperText Transfer

A Computer Remote Control System 1003

Fig. 1. Android system architecture.

Fig. 2. Data flow diagram of speech recognition.

Protocol) POST 1. Then the server performs NLP. Data flow diagram of speech recogni-tion in Figure 2 shows that there are several steps involved in NLP as in the following:

1. Lexical Analysis converts sequence of characters into a sequence of tokens.2. Morphological Analysis identifies, analyzes, and describes the structure of a given

language’s linguistic units.1 POST request is used to send data to a server. The string detected by speech recognizer is

passed to the server using this method. It accomplishes this using in-built HttpCore API (i.e.,org.apache.http package). The server performs processing and returns a JSON (JavaScriptObject Notation) response. JSON is a lightweight data-interchange format, is based on a sub-set of the JavaScript programming language, and is completely language independent. In Java,org.json.JSONObject is used to parse strings [1].


3. Syntactic Analysis analyzes texts, which are made up of a sequence of tokens, todetermine their grammatical structure.

4. Semantic Analysis relates syntactic structures from the levels of phrases and sen-tences to their language-independent meanings.

Hidden Markov Model. Modern general-purpose speech recognition systems are basedon Hidden Markov Models (HMM). HMM is a doubly stochastic process with an un-derlying stochastic process that is not observable (it is hidden), but can only be observedthrough another set of stochastic processes that produce the sequence of observed symbols[4], [11]. HMMs are statistical models that output a sequence of symbols or quantities,and are used in speech recognition because a speech signal can be viewed as a piecewisestationary signal or a short-time stationary signal. In a short time-scales (e.g., 10 millisec-onds), speech can be approximated as a stationary process. Speech can be thought of as aMarkov model for many stochastic purposes [15]. Another reason why HMMs are popularis because they can be trained automatically and are simple and computationally feasibleto use. In speech recognition, the hidden Markov model would output a sequence of n-dimensional real-valued vectors (with n being a small integer, such as 10), outputting oneof these every 10 milliseconds. The vectors would consist of cepstral coefficients, whichare obtained by taking a Fourier transform of a short time window of speech and de-correlating the spectrum using a cosine transform, then taking the first (most significant)coefficients. The hidden Markov model will tend to have in each state a statistical distri-bution that is a mixture of diagonal covariance Gaussians, which will give a likelihood foreach observed vector. Each word, or (for more general speech recognition systems), eachphoneme, will have a different output distribution; a hidden Markov model for a sequenceof words or phonemes is made by concatenating the individual trained hidden Markovmodels for the separate words and phonemes.

The following notations for a discrete observation HMM are defined.Let T = {1, 2, · · · , T} be the observation sequence (i.e., number of clock times), and Tis length of the observation sequence. Let Q = {q1, q2, · · · , qN} be states, where N is thenumber of states, V = {v1, v2, · · · , vM} be discrete set of possible symbol observations,where M is the number of possible observations, A = {aij} be state transition probabilitydistribution, where aij = Pr(qi at t + 1|qi at t), B = {bj(k)} be observation symbolprobability distribution in state j, where bj(k) = Pr(vk at t|qj at t), and π = {πi} beinitial state distribution, where πi = Pr(qi at t = 1) [11].

The mechanism of the HMM is explained in the following:

– Step-1. Choose an initial state, i1, according to the initial state distribution, π.– Step-2. Set t = 1.– Step-3. Choose Ot, according to bit(k), the symbol probability distribution in stateit.

– Step-4. Choose it+1 according to {aitit+1}, it+1 = 1, 2, · · · , N , the state transitionprobability distribution for state it.

– Step-5. Set t = t+ 1; return to Step-3 if t < T ; otherwise terminate the procedure.

We use the compact notation λ = (A,B, π) to represent an HMM. For every fixedstate sequence I = i1i2 · · · iτ , the probability of the observation sequence O is Pr(O|I, λ),where

Pr(O|I, λ) = bi1(o1)bi2(o2) · · · biτ (oτ ). (1)


In other words, the probability of such a state sequence I is

Pr(I|λ) = πi1ai1i2ai2i3 · · · aiτ−1iτ . (2)

The joint probability of O and I is simply the product of the above two terms,

Pr(O, I|λ) = Pr(O|I, λ)Pr(I|λ). (3)

Then the probability of O is obtained by summing this joint probability over all possiblestate sequences:

Pr(O|λ) =∑

allI

Pr(O|I, λ)Pr(I|λ) (4)

=∑

i1,i2,···,iτπi1bi1(o1)ai1i2bi2(o2) · · · aiτ−1iτ biτ (oτ ). (5)

Neural Networks. Neural networks emerged as an attractive acoustic modeling approachin automatic speech recognition (ASR) in the late 1980s. Since then, neural networks havebeen used in many aspects of speech recognition such as phoneme classification, isolatedword recognition, and speaker adaptation [12], [15]. In contrast to HMMs, neural net-works make no assumptions about feature statistical properties and have several qualitiesmaking them attractive recognition models for speech recognition. When used to estimatethe probabilities of a speech feature segment, neural networks allow discriminative train-ing in a natural and efficient manner. Few assumptions on the statistics of input featuresare made with neural networks. However, in spite of their effectiveness in classifyingshort-time units such as individual phones and isolated words, neural networks are rarelysuccessful for continuous recognition tasks, largely because of their lack of ability tomodel temporal dependencies. Thus, one alternative approach is to use neural networksas a pre-processing e.g. feature transformation, dimensionality reduction, for the HMMbased recognition.

Other Speech Recognition Systems. Modern speech recognition systems use variouscombinations of a number of standard techniques in order to improve results over thebasic approach described above. A typical large-vocabulary system would need contextdependency for the phonemes (so phonemes with different left and right context havedifferent realizations as HMM states). It would use cepstral normalization to normalize fordifferent speaker and recording conditions. For further speaker normalization it might usevocal tract length normalization (VTLN) for male-female normalization and maximumlikelihood linear regression (MLLR) for more general speaker adaptation. The featureswould have so-called delta and delta-delta coefficients to capture speech dynamics andin addition might use heteroscedastic linear discriminant analysis (HLDA); or might skipthe delta and delta-delta coefficients and use splicing and a linear discriminant analysis(LDA)-based projection followed perhaps by heteroscedastic linear discriminant analysisor a global semi-tied covariance transform (also known as maximum likelihood lineartransform, or MLLT). Many systems use so-called discriminative training techniques thatdispense with a purely statistical approach to HMM parameter estimation and insteadoptimize some classification-related measure of the training data. Examples are maximummutual information (MMI), minimum classification error (MCE) and minimum phoneerror (MPE) [10], [15].


Fig. 3. Arirang note that is lyrical folk song in the Republic of Korea [14].

3. Comparison of Speech Recognition Rate

We have investigated how much recognition rates of current speech recognition systems,including Google speech recognition, NHN (Naver), Q Voice, S Voice, and Siri, are withArirang 2, lyrical folk song in the Republic of Korea; and also see Arirang note in Figure3.

One hundred replications in Korean were tested for each speech recognition system.According to our investigation, Table 1 shows that Google speech recognition system isthe best of five speech recognition systems. Thus, it was used to design and implementour proposed system.

4. Implementation and Results

Figure 4 shows the architecture of the proposed system and command transmission meth-ods among a mobile device, a Google server, and a personal computer server. The rolesof each number are in the following:

1. A user commands using the speech recognition application of the mobile device.2 Arirang is a popular form of Korean folk song and the outcome of collective contributions made

by ordinary Koreans throughout generations. Essentially a simple song, it consists of the refrain’Arirang, arirang, arariyo’ and two simple lines, which differ from region to region [14].


Table 1. Comparison of speech recognition rate for speech recognition systems.

Speech recognition Recognition Smartphone Smartphone Techniques usedsystem rate (%) type version

Google speech 100 Galaxy III Android4.1.2 Google’s ownrecognition technology

NHN(Naver) 51 Galaxy III Android4.1.2 LinkQ Voice 98 Optimus G Android4.1.2 1st step: Google

2nd step: WernickeS Voice 96 Galaxy III Android4.1.2 Vlingo

Siri 94 IPhone 5 IOS 6.1 Nuance

Fig. 4. Command transmission methods among a mobile device, a Google server, and apersonal computer server.

2. Execute STT (speech to text) through the Google server.3. Transmit results obtained from STT to the mobile device.4. Transmit results obtained from STT to the personal computer server via wireless com-

munications such as 3G, WIFI, and Bluetooth.5. The personal computer server analyzes corresponding commands, and executes to

distinguish between information which is sent to the Google server, and informationwhich is executed on the personal computer server.

6. Transmit information to the Google server if there is information to use the Googleserver among commands.

7. The Google server returns corresponding values after analyzing corresponding ser-vices.

8. Give the user information received from the Google server with voice messages orexecute.

Figure 5 shows overall use case diagram of the proposed system that contains morethan five main functions such as speech recognition, keyboard control, mouse control,simple mode, and text transmission.


Fig. 5. Overall use case diagram of the proposed system.


Our proposed computer remote control system using speech recognition technologiesof mobile devices and wireless communication technologies was implemented by Javaprogramming language. The proposed application was designed and developed on An-droid as well.

4.1. Speech Recognition ModeThe below program code shows Java code of speech recognition for the proposed ap-plication. startVoiceRecognitionActivity fires an intent to start the speechrecognition activity and onActivityResult handles the results from the recognitionactivity.

private void startVoiceRecognitionActivity() {

Intent intent = new Intent(RecognizerIntent.ACTION_RECOGNIZE_SPEECH);

intent.putExtra(RecognizerIntent.EXTRA_LANGUAGE_MODEL,

RecognizerIntent.LANGUAGE_MODEL_FREE_FORM);

intent.putExtra(RecognizerIntent.EXTRA_PROMPT, "Speech recognition demo");

startActivityForResult(intent, VOICE_RECOGNITION_REQUEST_CODE);

}

@Override

protected void onActivityResult(int requestCode, int resultCode, Intent data) {

if (requestCode == VOICE_RECOGNITION_REQUEST_CODE && resultCode == RESULT_OK) {

// Fill the list view with the strings the recognizer thought it could have heard

ArrayList<String> matches =

data.getStringArrayListExtra(RecognizerIntent.EXTRA_RESULTS);

mList.setAdapter(new ArrayAdapter<String>(this, android.R.layout.simple_list_item_1,

matches));

}

super.onActivityResult(requestCode, resultCode, data);

}

Figure 6 shows speech recognition by touching the mobile device screen. When ex-ecuting speech recognition by touching the top of the mobile device screen, all speechcontents are typed and saved on the computer. When executing speech recognition bytouching the bottom, corresponding service is executed by recognizing all speech con-tents. For example, a user commands the mobile device to do ’what is today’s weather?’and then the remote system answers ’Today is 20 degrees Celsius and the weather is fine.’Another example is that a user from the outside commands his/her mobile device to do’Send meeting document in the document folder.’ and then the system finds it in the folderand transmits it to user’s mobile device or a personal computer that he/she wants.

4.2. Keyboard Control ModeFigure 7 demonstrates computer keyboard control by touching the smartphone screen. Acomputer’s keyboard is controlled by a method that the key value entered by the user istransmitted from smartphone (client) to PC (server) through socket communication. TheQWERTY keyboard, which is the most common modern-day keyboard layout, consistsof XML. Each button has an independent OnClickListener, and depending on thestate of the keyboard, transmitted values are different.

User-entered key values with the specified protocol (”$”) are sent to PC (server). Thereceived values are stored on the PC (server) using keypress () and keyRelease() methods of the Robot class in Java.


Fig. 6. Speech recognition by touching the smartphone screen.

4.3. Mouse Control Mode

Figure 8 presents computer mouse control by touching the smartphone screen. There aredouble click, left click, and right click buttons. In order to control the mouse, using thetouch screen of the smartphone (client), with UDP, the remote computer control systemtransmits the first coordinate and an actuated coordinate. In case of the mouse control,with UDP, speed rather than accuracy is prioritized because the system has to quicklytransmit data. Using the mouseMove () method of the Robot class in Java, the systemremotely controls user’s PC mouse pointer on PC (server) that was received the transmit-ted coordinates.

4.4. Simple Mode

Execution of applications users want on the simple mode is shown in Figure 9. Whileusing a computer, there are programs that you often use, such as explorer, notepad, Hangul(Korean) word processor, GOM Player, and messenger. The Simple mode is the mode ofexecution that these programs are executed with a single click from a remote location.When the button is clicked on smartphone (client), the commands will be sent to thePC (server) through TCP communication. Using the exec () method of the Runtimeclass in Java, with the touch of a button, the program that you want will be easily executedon PC received the commands through the external command.


(a) Use case diagram of keyboard control

(b) Screenshot of keyboard control

Fig. 7. Computer keyboard control by touching the smartphone screen.

4.5. Sending a Text Message Mode

The existing service method, which has transmitted texts through voice, does not readtexts entered by the user and send back to the user. The proposed system, however, usingthe STT technology, provides the function that can correctly deliver the information sincewhen the user inputs his/her voice on smartphone, it re-reads what you enter through theTTS function. When you have made all your input through SmsManager, the systemsends a text message to the other party; and also see that Figure 10 shows a flowchart howto send a text message.


(a) Use case diagram of mouse control

(b) Screenshot of mouse control

Fig. 8. Computer mouse control by touching the smartphone screen. There are doubleclick, left click, and right click buttons.

4.6. Other Modes: File Search Mode

File search function is the ability to look at contents in the hard drive of the PC Server onsmartphone. When smartphone users (Client) request a list of files in the PC (server), us-ing the File class in Java, the proposed system distinguishes files and folders, and sends thelist to the smartphone. This list with the folders and files shows on the smartphone screenthrough ListView. When the user clicks a folder, its contents shows in ListView.When the user clicks a file, the file is run through the exec () method of Robot classon the PC Server. For example, when requesting to send test.pdf file from your smart-phone, the test.pdf file, which is sent to your smartphone, can be found.


(a) Use case diagram of simple mode

(b) Screenshot of simple mode

Fig. 9. Execution of applications that users want to on the simple mode.

4.7. Other Modes: PC Screen Check Mode

When smartphone users request the transfer of your PC screen, the proposed system cap-tures the current screen using the Robot class on PC, and transmits the screen to thesmartphone through TCP communication. The smartphone receives the file and shows iton the ImageView screen. The multi-touch is possible, zooming in and out is feasible,and the system can check what the current PC’s screen is. Commands with speech recog-nition are available, and a remote control mode in real time is possible by making sure thePC’s screen.


Fig. 10. Flowchart of sending a text message.

5. Conclusion

A computer remote control system using speech recognition technologies of mobile de-vices and wireless communication technologies for the blind and physically disabled pop-ulation has been proposed. These people experience difficulty and inconvenience in using


computers through a keyboard and/or mouse. The major purpose of this system was toprovide a system so that the blind and physically disabled population can easily con-trol many functions of a computer via speech. The system is very useful for the generalpopulation as well. Users command a mobile device to do something via speech suchas directly controlling computers, writing emails and documents, calculating numbers,checking the weather forecast, or managing the schedule. These commands are then im-mediately executed. The proposed system also provides blind people with a function viaTTS (text to speech) of the Google server if they want to receive contents of a documentstored in a computer.

Acknowledgments. The authors would like to give thanks to the funding agencies for providingfinancial support. Parts of this work were supported by a research grant from Korean Bible Univer-sity. The authors also thank Robert Hotchkiss and three referees for their constructive remarks andvaluable comments.

References

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3. Android: Android Operating System, Wikipedia. http://en.wikipedia.org/wiki/Android OS4. Jarng, S.S.: Analysis of HMM Voice Recognition Algorithm. Journal of Advanced Engineering

and Technology 3(3), 241–249 (2010)5. Jeong, H.D., Lim, J., Hyun, W., An, A.: A Real-time Location-based SNS Smartphone Ap-

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6. Jeong, H.D., Ye, S.K., Lim, J., You, I., Hyun, W., Song, H.K.: A Remote Computer ControlSystem Using Speech Recognition Technologies of Mobile Devices. In: The Seventh Interna-tional Conference on Innovative Mobile and Internet Services in Ubiquitous Computing: Fu-ture Internet and Next Generation Networks (FINGNet-2013). pp. 595–600. Taichung, Taiwan(2013)

7. Knight, W.: Where Speech Recognition Is Going. MIT Technology Review, technologyre-view.com (2012)

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12. Tan, Z.H., Varga, I.: Network, Distributed and Embedded Speech Recognition: An Overview.Advances in Patterns Recognition (2008)

13. Torrente, J., A.d. Blanco, A. Serrano-Laguna, Vallejo-Pinto, J., Moreno-Ger, P., Fernandez-Manjon, B.: Towards a Low Cost Adaptation of Educational Games for People with Disabili-ties. Computer Science and Information Systems (ComSIS) 11(1), 369–391 (2014)


14. UNESCO: Arirang, lyrical folk song in the Republic of Korea.http://www.unesco.org/culture/ich/RL/00445

15. Wikipedia: http://en.wikipedia.org/wiki/Speech recognition

Hae-Duck Joshua Jeong is an associate professor in the Department of Computer Soft-ware at Korean Bible University, Seoul, South Korea. He received his Ph.D. in ComputerScience and Software Engineering from the University of Canterbury, New Zealand. Heis the author or co-author of more than 85 research publications, including more thantwenty patents. Dr. Jeong is on the editorial board and a reviewer for various domesticand international journals. He is the corresponding guest editor or guest editor of Com-SIS and MCM. His research interests include teletraffic modeling, stochastic simulation,multimedia telecommunication networks, intrusion detection system, social networkingservice, and real-time system. Member of IEEE NZ, KIPS, KSII, and ORSNZ.

Sang-Kug Ye is a student in the Department of Computer Software at Korean Bible Uni-versity, Seoul, South Korea. He is the author or co-author of more than 5 research publica-tions, including two patents. He received the President’s Computer Software CompetitionAward four times including the grand prize twice from Korean Bible University from2011 to 2013. His research interests include network security, intrusion detection system,and mobile applications.

Jiyoung Lim received her Ph.D. degree in Computer Science and Engineering from EwhaWomans University in 2001. She is currently an associate professor of Computer Soft-ware at Korean Bible University, Seoul, South Korea. Her research interests include wire-less/sensor network security, and M2M network security.

Ilsun You received his Ph.D. degree in Computer Science from Dankook University,Seoul, South Korea in 2002. Also, he obtained his second Ph.D. degree from KyushuUniversity, Japan in 2012. In 2005, he joined Korean Bible University, South Korea asa full time lecturer, and he is now working as an associate professor. Dr. You is on theeditorial board for various domestic and international journals. Also, he has served as aguest editor of several journals. His main research interests include Internet security, au-thentication, access control, formal security analysis, MIPv6, and ubiquitous computing.

Wooseok Hyun is the corresponding author of this paper. She is an associate professorin Computer Software at Korean Bible University, Seoul, South Korea. She received herPh.D. in Computer Science from Gyeongsang National University, South Korea. She isthe author or co-author of more than 30 research publications, including five patents;reviewer of various domestic and international journals. Her research interests includeubiquitous computing, intelligent system, fuzzy system, information retrieval system, andartificial intelligence. Member of KIISE, KIPS, KMMS.


A New Hybrid Architecture with an Intersection-Based Coverage Algorithm in Wireless Sensor Networks

Abstract.

Keywords:

1. Introduction

2. Related Work

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Acknowledgments.

References

Young-Long Chen

Mu-Yen Chen

Fu-Kai Cheung

Yung-Chi Chang


The Efficient Implementation of Distributed Indexing with Hadoop for Digital Investigations on Big Data

Abstract.

Keywords:

1. Introduction

Fig. 1.

2. Related Works

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Fig. 2.

3. Implementation Method for Efficient DTPS with Hadoop

3.1. Prototype Design of DTPS

Fig. 3.

3.2. Basic Requirements

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Table 1.

light

light

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Fig. 4.

3.3. Implementation Strategy for Differentiated Experimental Design

Table 2.

Fig. 5.

4. Evaluation Results and Analysis

4.1. Configurations of DTPS

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light

4.2. Test Results and Analysis

Table 3.

Table 4.

Table 5.

5. Conclusions

Acknowledgement.

References

light

Taerim Lee

Hyejoo Lee

Kyung-Hyune Rhee

Sang Uk Shin


A New Detection Scheme of Software Copyright Infringement using Software Birthmark on Windows

Systems

Abstract.

Keywords:

1. Introduction

Dynamic Link Libraries Application Programming Interfaces

Portable ExecutableImport Address Table

pi

pipi

pi

pi

2. Background and Related Work

2.1. Import Address Table

2.2. MD5 (Message-Digest algorithm5) Hash Function

Fig. 1.

2.3. Related Work

3. The Proposed Software Birthmark

��

Fig. 2.

Fig. 3.

Fig. 4.

4. Software Filtering using the Software Birthmark

4.1. Identifying and Filtering Overview

Fig. 5.

4.2. Detailed Steps

pi

Step 1:

Step 2:

Step 3:

Step 4:

5. Experiments and Evaluation

5.1. Target Applications

Table 1.

5.2. Identifying the Target Applications

Table 2.

Case 1:

Case 2:

5.3. Measuring the Time to Identify an Application

Fig. 6.

6. Conclusion and Future Work

Acknowledgements.

References

Yongman Han

Jongcheon Choi

Seong-je Cho

Haeyoung Yoo

Jinwoon Woo

Yunmook Nah

Minkyu Park


Pairwise and Group Key Setup Mechanism for Secure Machine-to-Machine Communication

Abstract.

Keywords:

1. Introduction

Table 1.

��

�

2. Related Works

t

3. System Architecture

Fig. 1.

Fig. 2.

4. Key Establishment for M2M Communication

Pairwise Keys between an eNB and an M2ME.

Pairwise Keys between M2MEs.

Functional Group Keys for M2MEs.

Regional Group keys for M2MEs.

4.1. Key Establishment between eNB and Mobile M2ME

Fig. 3.

4.2. Key Establishment between a Pair of M2MEs for Direct Communication

Fig. 4.

�

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Fig. 5.

4.3. Group Key Establishment among M2MEs for Group Communication

Fig. 6.

�

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Functional Group Key.

Regional Group Key.

�

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5. Performance Analysis

5.1. Simulation Result

Fig. 7.

0 200 400 600 800 1000

0.0030

0.0035

0.0040

0.0045

0.0050

0.0055

0.0060

0.0065

0.0070

Tran

smis

sion

Tim

e(se

c)

Transmission time between M2MEs by different distance

transmission time

Fig. 8.

M2ME 1 (sending node)

M2ME 2 (receiving node)

eNB0.000

0.005

0.010

0.015

0.020

0.025

0.030

0.035

Ene

rgy

Usa

ge

Energy usage when M2MEs communicate directly

energy when receiving energy when sending

Fig. 9.



eNB0.000

0.005

0.010

0.015

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0.025

0.030

0.035

0.040

0.045

ener

gy (m

Wh)

)

Energy usage when M2MEs communicate indirectly through eNB


Fig. 10.



eNB0.00

0.01

0.02

0.03

0.04

0.05

0.06

0.07

ener

gy(m

Wh)

Energy usage when eNB sends key setup information to M2MEs


Fig. 11.

5.2. Communication and Computation Analysis

5.3. Security Analysis

Confidentiality.

Authentication.

Access Control.

Integrity.

Privacy.

6. Conclusions

Acknowledgments.

References

Inshil Doh

Jiyoung Lim

Shi Li

Kijoon Chae

Received September 22 2013 Accepted February 18 2014.

A Secure E-Mail Protocol Using ID-based FNS Multicast Mechanism

Abstract.

Keywords:

1. Introduction

BA B BS

BS A AS

AS AAS A

AS A

A C DB BS

BS AS CS DS

AS CS DS

CSC CS C

2. Fundamental Theory of the ID-based FNS

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Theorem 1

Example 1. I � I � I � I � I �

u u u u uu

I u u u uu p� � p� �

p� � p� � p� � I I I I I Lemma 1 u of * * *P ,P ,...,P as

the followings, ii

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3. Proposed Scheme

Table 1.

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C k)m(Sigk

m k)(h �

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3.1. Scenario I: A sender sends an e-mail to one recipient

Fig. 1.

iUTC � g n TC gn iU ik

nmodge i

i

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j

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Fig. 2.

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�� Y �Y

4. Security and Complexity Analysis

4.1. Security Analysis

Replay Attack.

t

Sender Impersonation Attack.

Y

Unknown Key-Share Attack.

Yt

Y

Forgery Attack.

Insider Attack.

5. Computation Complexity

m

� �

� �

Table 2.

� 80� m m m

� 258� (m+2)

m m m

XOR

Table 3. �

m

m

m

m

m

�

Fig. 3. �

�

Table 4. �

Fig. 4. �

Table 5.

Fig. 5.

Table 6.

Fig. 6.

6. Conclusions

Acknowledgments.

References

Hsing-Chung Chen (Jack Chen)

Cheng-Ying Yang

Hui-Kai Su

Ching-Chuan Wei

Chao-Ching Lee


Study on Network Architecture of Big Data Center for the Efficient Control of Huge Data Traffic

Abstract.

Keywords:

1. Introduction

Fig. 1.

2. Problems Caused by Big Data Traffic

3. The Related Researches for the Separation of Big Data Traffic

4. The Paradigm Shifts of Network Architecture for Big Data Center

4.1. The 1st Paradigm Shift on Resource Provisioning

Fig. 2.

Fig. 3.

4.2. The 2nd Paradigm Shift on Service Provisioning

Fig. 4.

Fig. 5.

4.3. The 3rd Paradigm Shift on QoS Provisioning

Fig. 6.

Fig. 7.

Fig. 8.

4.4. The Candidate Network Architecture for Big Data Center

Fig. 9.

5. Simulations for the Analysis of the Impact of Big Data Traffic

Fig. 10.

Table 1.

Fig. 11.

Fig. 12.

Fig. 13.

Fig. 14.

Fig. 15.

Fig. 16.

6. Conclusion

Acknowledgements.

References

Hyoung Woo Park

Il Yeon Yeo

Jongsuk Ruth Lee

Haengjin Jang


Computer Science and Information Systems 11(3):1127–1141 DOI: 10.2298/CSIS130921068W

An Efficient Wormhole Attack Detection Method inWireless Sensor Networks

Guowei Wu1, Xiaojie Chen1, Lin Yao1, Youngjun Lee2, and Kangbin Yim2

1 School of Software, Dalian University of Technology,Dalian, 116620 China

[email protected], [email protected], yaolin [email protected] Dept. of Information Security Engineering, Soonchunhyang University,

Asan, 336-745 [email protected], [email protected]

Abstract. Wireless sensor networks are now widely used in many areas, such asmilitary, environmental, health and commercial applications. In these environments,security issues are extremely important since a successful attack can cause greatdamage, even threatening human life. However, due to the open nature of wirelesscommunication, WSNs are liable to be threatened by various attacks, especially de-structive wormhole attack, in which the network topology is completely destroyed.Existing some solutions to detect wormhole attacks require special hardware orstrict synchronized clocks or long processing time. Moreover, some solutions can-not even locate the wormhole. In this paper, a wormhole attack detection methodis proposed based on the transmission range that exploits the local neighborhoodinformation check without using extra hardware or clock synchronizations. Exten-sive simulations are conducted under different mobility models. Simulation resultsindicate that the proposed method can detect wormhole attacks effectively and effi-ciently in WSNs.

Keywords: wormhole attacks, wireless sensor network, local neighborhood, net-work topology.

1. Introduction

Wireless sensor networks (WSNs) consist of a large number of low-cost and resourceconstraint sensor nodes to perform distributed sensing tasks. Sensor nodes in WSNs col-laborate with each other to transmit messages in a multi-hop manner. WSNs are usedfor various tasks such as surveillance, widespread environmental sampling, security, andhealth monitoring [23][2]. WSNs are characterized by their infrastructure-less nature, easeof deployment and independence to any pre-existing architecture [24]. Since the open na-ture of wireless communication, WSNs are prone to be attacked in various ways, such asDenial of Service (DOS) attack, the wormhole attack, the Sybil attack, selective forward-ing attack, etc. [22].

In this paper, the wormhole attack [1][5][10] is taken into consideration. The worm-hole attack is a kind of tunneling attack, which is very dangerous and damaging to defendagainst even though the routing information is confidential, authenticated or encrypted[9]. The adversary doesn’t need to have knowledge about the routing protocols or com-promise the sensor nodes. In wormhole attack, two malicious nodes are connected through

1128 Guowei Wu et al.

a low-latency link, namely wormhole link. A low latency can be realized through a net-work cable, other kind of wired link technology or just a long-range out-of-band wirelesstransmission [20]. Once the wormhole link is established, the adversary eavesdrops onpackets at one end of the link, tunnels them through the wormhole link and replays thepackets at the other end of the link. This makes the sensor nodes around the two ends ofthe wormhole link seem like neighbor nodes as though they are multi-hops away fromeach other actually.

Fig. 1. The Minimum Key Set Route

An example of wormhole attack is given in Fig.1. Node A and B are two maliciousnodes placed by the adversary connected via a network cable. So node A and B are thetwo end points of the wormhole link. Node A receives packets, tunnels them through thewormhole link and replays the packets at node B and vice versa. As a result, nodes in theneighborhood of node A will assume that all nodes in the neighborhood of node B aretheir neighbors and vice versa. For example, source node s can take a one-hop path tosend packets to destination node d via the wormhole link instead of a multi-hop path.

A number of protocols have been proposed to defend against wormhole attacks inwireless networks by adopting synchronized clocks, positioning devices, or directionalantennas [19]. In this paper, we introduce novel approaches for detecting wormhole at-tacks and propose an efficient wormhole detection algorithm, which is named Transmis-sion Range based Method (TRM). With the existence of wormhole, the network topol-ogy is destructed and normal routes are misled. Unlike many existing techniques, it doesnot use any specialized hardware, making it extremely useful for real-world scenarios.Most importantly, however, the algorithm can always prevent wormholes, irrespective ofthe large transmission range, by checking the local neighborhood information to decidewhether the network topology is true or faked, while its efficiency is not affected even bythe dynamic topology. We also provide an analytical evaluation of the algorithm’s cor-rectness through simulation experiments that demonstrates its efficiency in terms of com-putation complexity and processing delay. The remainder of this paper is organized asfollows. In Section 2, related works are discussed. The wormhole attack detection methodis presented in Section 3. The performance of our method is evaluated through simulationexperiments in Section 4. At last, we conclude our work in Section 5.

An Efficient Wormhole Attack Detection Method in Wireless Sensor Networks 1129

2. Related Works

Wormhole attack is very destructive since the neighborhood information is confused. Anyrouting protocol relying on network topology information can’t work normally. Periodicprotocols like Secure Efficient Ad hoc Distance vector routing protocol (SEAD) [6] willmalfunction because the routing table information is different from the real network topol-ogy due to the wormhole. On-demand protocols like Dynamic Source Routing protocol(DSR) [11] will have false route establishment because the route quest and route replymessage in the route discovery stage will contain the wormhole link. So all the routesestablished by these network routing protocols are attracted to the wormhole and the ad-versary can launch further attack like selective forwarding attack, black hole attack andetc. What is worse, the wormhole attack is easily deployed to some extent. The adver-sary has no need to compromise any node in the network and don’t need to deal with thecryptographic keys. The integrity, authenticity and confidentiality are still reserved in theexistence of wormhole. All the adversary has to do is to place two malicious nodes ingood positions in the network and make them receive and send packets.

Because of the reason, the detection of wormhole attack has become an essential is-sue and various methods have been proposed to detect the wormhole. In [7], Hu et al.introduce the general mechanism of packet leashes to detect wormhole attacks. Two typesof leashes are used: geographic leashes and temporal leashes. A leash is any informationthat is added to a packet designed to restrict the packet’s maximum allowed transmissiondistance. However, to form a leash, each node must know its own location and have syn-chronized clocks. In [8], the End-to-end Detection of Wormhole Attack (EDWA) is pro-posed in wireless ad-hoc networks. The source node estimates the minimum hop count tothe destination and compares the hop count value received from the reply packet to detectthe wormhole. Obviously, each node should measure its geographical location through aGPS. There are some solutions based on the discovery and maintenance of node neighbor-hood. For instance, LITEWORP [12] uses secure two-hop neighbor discovery and localmonitoring of control traffic to detect nodes involved in the wormhole attack. It providesa countermeasure technique that isolates the malicious nodes from the network therebyremoving their ability to cause future damage. MobiWorp [13] is further proposed to com-plement LITEWORP by introducing some location-aware mobile nodes.

Most existing solutions are based on the network topology. Lazos et al. [14] presenta graph-based framework to tackle wormhole attacks. Making use of geometric randomgraphs induced by the communication range constraint of the nodes, the authors presentthe necessary and sufficient conditions for detecting and defending against wormholes.In [16], the authors propose a wormhole detection approach with only local connectiv-ity information. The algorithm uses only connectivity information to look for forbiddensubstructures in the connectivity graph. In [4] a distributed connectivity-based wormholedetection method is proposed. Each node collects its k-hop neighborhood and checkswhether the boundary of its k-hop neighborhood sub-graph has one or two circles. Itsbasic idea is based on the observation that the neighborhood that encloses a wormholelink will have two cycles and single cycle otherwise. In [3], authors develop a simple dis-tributed algorithm for wormhole detection in wireless ad hoc and sensor networks, usingonly the communication graph, and not making unrealistic assumptions. Their algorithmworks well in relatively dense and regular networks but results in many false positives insparse or random networks. In [15], each node locally collects its neighborhood informa-


tion and reconstructs the neighborhood sub-graph by Multi-Dimensional Scaling (MDS).Potential wormhole nodes are detected by validating the legality of the reconstruction.Then, a refinement process is introduced to filter the suspect nodes and to remove falsepositives. In the paper [21], wormhole attack detection is proposed based on Round-TripTime (RTT) between successive nodes and congestion detection mechanism. If the RTTbetween two successive nodes is higher than the threshold value, a wormhole attack issuspected. If a wormhole is suspected, node’s transitory buffer is probed to determinewhether the long delay between the nodes is due to wormhole or not, as delays can becaused due to congestion or by queuing delays.

3. Proposed wormhole detection method

Detecting wormholes in WSNs is essential since they can make the routing protocolsmalfunction. In this paper, a highly efficient wormhole detection method named TRM isdeveloped, which uses the local neighborhood information to calculate the transmissionrange.

3.1. Network model

In order to prepare for the discussion of the wormhole detection, the network model ispresented first. In the network model, a WSN with N sensor nodes is considered, whichcan be denoted by a directed graph G = (V,E). In this graph, V is the set of verticesindicating the sensor nodes and E is the set of direct edges indicating the wireless links inthe graph. The graph takes a Unit Disk Graph (UDG) [17] as its connectivity model. InUDG, each node is modeled as a disk of unit radius in the plane, which indicates the trans-mission range of a single node. Each node is a neighbor of all nodes located in its disk.Nodes are randomly distributed in the specified area. Two types of nodes are considered inthe network: normal nodes and malicious nodes placed by the adversary. Malicious nodesdiffer from normal nodes in their transmission range, power and calculation capability.

3.2. Adversary model

As described in Section 1, one end of the wormhole eavesdrops on packets, tunnels themthrough the wormhole and replays them at the other end of the wormhole. The adversarycan place many pairs of malicious nodes to deploy wormholes across the whole network.The adversary’s goal is to attract as more routes through the wormhole link as possible.And as long as the wormholes are placed carefully, the majority of the network routes canbe attracted to the wormhole link. To introduce our wormhole detection method, someassumptions must be made first. These three assumptions following lay a foundation forour wormhole detection method.

1. The wormhole link is long enough so the regions of the two end points don’t overlapwith each other [17]. For example, A and B in Fig.1 are well separated from eachother, i.e., they are multi-hops away.


2. There is some time t when the wormhole is absent, so the sensor nodes have enoughtime to establish their neighbors.

3. The wormhole is closed [25]. The wormhole attacks are divided into three groups(closed, half open, and open) according to the format of the tunnel and attacker’scapability. In this paper, we focus on the closed wormhole attack.

3.3. Principle and analysis

In order to explain our wormhole detection method, its principle analysis is presentedfirst. In the network, each node pair can establish a link because their distance is less thanor equal to the transmission range r. For any node m, the neighbor set of m is denotedby N(m). For example, if a node B can receive packets from node A with one hop, nodeB is a neighbor of node A and meets B ∈ N(A). The principle is to check the neighbortopology by using the geometric relationship of nodes’ locations under the constraint ofthe communication range of the two involved sensor nodes.

Fig. 2. Neighbor Nodes without Wormhole

The principle is illustrated in Fig.2 by studying the geometric relationship amongnodes in the network without wormholes. Node A and B are two neighbor nodes to bechecked. Node C meets C ∈ N(B) but C �∈ N(A). The transmission range of node A,B and C is r. When node A adjusts its transmission range to R = 2r in Fig.2, all theneighbors of node C become neighbors of node A. So it meets that C ∈N(A) and N(B)⊆ N(A).

The geometric relationship among nodes in the network under wormhole attack istotally different as shown in Fig.3. Node A and B are two neighbor nodes which areconnected via the wormhole link. Node C and D both meet that C, D ∈ N(B). Node A,B and D are mutually neighbors due to the wormhole link as described in Section 1.

Node B and D lay in node A’s neighbor list due to the wormhole link. Node C isfar from the wormhole end point and thus free from wormhole attack. The transmissionrange of these four nodes is r at first. Then the transmission range of node A is expandedto R = 2r. Node D is node A’s neighbor connected by the wormhole link. However,since node A and B are multi-hops away from each other, node C is still not a neighborof node A even though the radius of node A is doubled. After increasing the radius of


Fig. 3. Neighbor Nodes with Wormhole

node A, one of node B’s neighbors is still not a neighbor of node A. So it meets that D ∈N(A) and C �∈ N(A). As a result, not all the neighbors of node B turn into neighbors ofnode A, which meets that N(B) �⊂ N(A). And this can be used to check whether thereexists a wormhole between two sensor nodes.

Fig. 4. Neighbor Nodes with Wormhole

Then we make some calculations to prove that the above principle is feasible. Asshown in Fig.4, the distance between A and B is denoted by d1; the distance between Band C is denoted by d2, the distance between A and C is denoted by d3. There are twocases of node-relative position: triangle and line. According to the neighbor relationshipdescribed above and their transmission range r, it is obvious that d1 ≤ r and d2 ≤ r.In the triangle case, it can be seen that d3 < d1 + d2 ≤ 2r. In the line case, it can beseen that d3 = d1 + d2 ≤ 2r. So we can get d3 ≤ 2r. Since the radius of node A isR = 2r, node C is within node A’s transmission range. And it meets that d3 ≤ 2r for∀C ∈ N(B). Therefore, we can get the formula N(B) ⊆ N(A). When the network isunder the wormhole attack, the actual distance of the two neighbor nodes A and B may bevery far away. It may meet that d3 > 2r for ∀C ∈ N(B). Node C is still not a neighborof node A after expanding its radius to 2r. But due to the wormhole, some node like Din Fig.3 may still be a neighbor of node A, which means that C �∈ N(A), ∃C ∈ N(B).Therefore, we can get N(B) �⊂ N(A). Now we can get the conclusion that:

1. When there is a wormhole and the transmission range of node A is R, there mustexist a node C ∈ N(B) but C �∈ N(A).


2. When there is no wormhole and the transmission range of node A is R, all nodesC ∈ N(B) meet C ∈ N(A).

3.4. Detection procedure

Based on the principle of detecting wormholes, detailed detection procedure will be pre-sented in this section. Two neighbor nodes such as node A and B are to be checkedwhich has its neighbor list N(A) and N(B) separately. The neighbor list informationcan be exchanged between neighbors through periodic beacon messages. After nodes Aand B exchange the neighbor list information, the detection procedure will begin. NodeA notifies all its neighbors in N(A) through its beacon messages that will increase itstransmission radius. The neighbor nodes receiving this notification will not change theirtransmission radius in the next beacon time. Then node A increases its transmission rangeto 2r and updates its neighbor list N(A). Finally, node A compares N(A) and N(B):

1. If the neighbor lists N(A) and N(B) satisfy N(B) ⊆ N(A), then there is no worm-hole link between node A and B.

2. If the neighbor lists N(A) and N(B) satisfy N(B) �⊂ N(A), then there is a worm-hole link between node A and B.

The node A and B in Fig.4 is used as two tested nodes to describe the main wormholedetection procedure of TRM algorithm. The flow of wormhole detection is shown in theFig.5. In our model, every node has a current list of its neighbors. Moreover, the neighborlist is regularly updated. Each node can request its neighbors to get their neighbor listsby transmitting a beacon message to its neighbors. Finally, each node can know one-hopneighbor information and two-hop neighbors as well. After a node starts the wormholedetection process, the node first broadcasts a beacon message including a packet to notifyits neighbors, which will increase the transmission range. All nodes receiving this notifi-cation will not change their transmission range in the next beacon period. After sendingthe message, the transmission range of node A is increased to 2r. If the neighbors of nodeB are still neighbors of node A, node A will search from the neighbor list in the nextbeacon period. If B’s one neighbor, node C, is still not a neighbor of A, a wormhole willbe detected. From Fig.5, we can see that communication links between nodes are requiredto establish in the primary stage. Then a node adopts the neighbor discovery mechanismto establish the link with other node. During the discovery stage, every node will sendits own neighbor list to its neighbors by sending beacon frames. By this way, each nodecan get its neighbor information within two hops. Finally, the network topology will beestablished. The beacon information will be transmitted at regular intervals. After chang-ing the radius, a test node will update its neighbor node list in the next beacon time. Bycomparing its current neighbor list with the previous list, a test node can find the existenceof false topology that does not exist in a normal network. Then the wormhole is detected.

In some wormhole detection methods based on statistical analysis, the algorithm cal-culates the link frequency statistics for some time to determine the presence of a worm-hole. This method must work after the routes are established and transmission is observedfor some time. TRM algorithm can begin execution before the route establishment phasecausing a large number of packets to be transmitted to the base station. In this way, worm-holes can be detected before the network traffic to be sent. Then the administrator of the


network can eliminate the bad effects of wormholes. The description of the algorithm isshown in Table 1.

Fig. 5. Wormhole Detection Process

3.5. Complexity and feasibility analysis

In order to demonstrate that our algorithm is a lightweight one, the complexity of thewormhole detection is analyzed from the aspects of time complexity and space complex-ity. The time complexity is the time consumed by executing the algorithm. In order toobtain the time complexity, the consumption time of detecting a pair of wormhole nodesis calculated firstly. Suppose there is a wormhole between node A and node B. The al-gorithm needs to find a node in N(B) but not in the neighbor list of node A. Since thenumber of neighbors is a constant c, the time complexity of wormhole detection is O(C),i.e., O(1). Secondly, the consumption time of detecting all pairs of wormhole nodes iscalculated. At this time, every node and its neighbors should be checked. When the num-


Table 1. Transmission Range based Method to Detect Wormholes

Line Description1 Given: Network N with node radius r, wormhole number c = 02 While check every node m in N do3 Expand radius of m to R = 2r4 For each node n in N(m) do5 If there exists once d ∈ N(n) and d �∈ N(m)6 then c+ 17 end for8 end while

ber of nodes is limited such as n and the number of its neighbors is c, the time complexityof TRM is O(cn), i.e., O(n).

The space complexity is defined as the storage space. In the TRM, the space com-plexity is influenced by the number of nodes in the network. According to our algorithm,except the neighbor list, no extra data structures are required to store in TRM. Supposethere is a wormhole between node A and node B. Because only neighbor information isstored, the space complexity is obviously O(1). When all the n nodes in the network arechecked, the space complexity is O(n). The feasibility of the algorithm is that every nodemust have its neighbor nodes. Suppose n nodes are distributed in a square region with theside length d and the transmission radius r. According to TRM algorithm, the number ofnodes in each row is

√n lying on a line of length d. The distance between two neigh-

bor nodes is d√n−1

. Every node can communicate with each other as long as the distancebetween neighbor nodes is less than the node’s transmission radius. So it should be met

d√n−1

≤ r, which is easy to implement. However, there may be some particularly isolatednodes, which doesn’t make sense for the wormhole attacker. In summary, the feasibilityof the proposed algorithm has been verified.

4. Simulation analysis

In order to verify the performance of our wormhole detection method, various experi-ments have been carried out. In the simulated system scenario, the wireless sensor net-work consists of 100 sensor nodes. First, we show the great damage of wormhole attack tothe network. Among the entire nodes, ten source nodes and ten destination nodes are se-lected randomly. Then routes are established between those source and destination nodes.The routes are set up using the basic Shortest Path Algorithm for simplicity. Then it canbe seen in the simulation as shown in Fig.5 that the routes are badly corrupted due to theexistence of wormhole. The routes are broken since the routes cross the wormhole endpoints. In this way, the traffic can be attracted to the wormhole link and the adversary canmount further attack like sinkhole attack or just eavesdrop on the information:

In the experiments, the nodes are distributed randomly in 5x5, 10x10, 15x15, and20x20 square separately. The node transmission range is 2 meters and nodes are dis-tributed randomly, which forms a unit disk graph for universality. The wormholes are alsoplaced in a random way.


Fig. 6. The Broken Routes Percent by Wormholes

Fig. 7. Simulation Results of Wormhole Detection Rate


In Fig.7, the wormhole detection rate is calculated as the number of wormholes in-creases from 2 to 40. The detection rate is also compared under different system scenarioin which the networks with the same number of wormholes have different node densities.Network distribution area 5x5 corresponds to the greatest node density. And the nodedensity decreases as the network distribution area increases to 10x10, 20x20, and 30x30.It can be seen from Fig.6 that the bigger the node density, the higher the detection rate.The detection rate is perfectly 100% when the side length of the network is square sinceit’s easy to detect wormholes when a node has many neighbors. A node’s detection failurecan be complemented by another neighbor node. The detection rate is not 100% becausesome neighbor nodes around the wormhole can’t detect the wormhole link. The detec-tion may fail because the node has nearly no neighbor to check the local neighborhoodinformation using our method. This situation, which is of low probability in practical ap-plication, happens in very spare network or some isolated sensor nodes. Moreover, thereis no worth for the adversary to attack such isolated sensor nodes because little traffic willbe caused to use by the attack.

To compare the performance of TRM with other wormhole detection method, twoother kinds of detection methods are simulated in the experiments. The TransmissionTime based Mechanism (TTM) [13] detects wormhole attacks during the route setup pro-cedure by computing transmission time between every two successive nodes along theestablished path. The Four Way Handshaking algorithm (FWH) [18] uses a simple four-way handshaking messages to exchange. It can be seen from Fig.8 when the wormholelength is smaller than 10, our method can achieve the highest detection rate. When thewormhole length is 2, the transmission time of two neighbor nodes created by wormholelink is not too long to be detected. The FWH algorithm is also affected by the time. OurTRM has nothing to do with the time and detect the wormholes according to the geomet-ric relationship of nodes as described in section 3. So our TRM can have high detectionrate all the time in different network scenarios.

Fig. 8. Detection Rate of Different Detection Methods


Fig. 9. Detection Time Comparison

In Fig.9, the detection time of TRM and TTM algorithm is compared. The actualaverage transmission time between one-hop nodes is ten milliseconds. However in TTM,the RTT between two nodes connected through the wormhole link is calculated since thetwo endpoints of wormholes are far away. In TTM, the detection result is obtained throughcalculating transmission time. So the detection time is longer when the wormholes are faraway. It can be seen in Fig.9 that the detection time increases greatly as the length betweenwormholes increases. In TRM, however, the wormholes are detected by checking thefalse neighbor topology. The wormholes can be found out by calculating the geometricalrelationship between nodes. In this way, the computation is of low complexity and morequick. At the same time, since the node’s neighbor list has nothing to do with the length ofwormholes, the wormhole length doesn’t affect the detection time. So the detection timedoesn’t increase greatly as the length of wormhole increases.

5. Conclusions

Wormhole attack in WSNs has been drawing more and more attention since it can disruptnormal network routing protocols. However, in previous work of wormhole detection,most of them need either extra hardware or clock synchronizations and suffer from highcomplexity. In this paper, an efficient wormhole detection method is proposed, which isbased only on local neighborhood information. Through judging the node’s position, wecan determine whether the node is in the local network topology affected by the wormholelink.

In the detection procedure, the neighborhood information of each node is updated andexchanged periodically between neighbors along with the increment of the transmissionrange. A local topology that has a wormhole link finally reports a mismatch of the neigh-borhood information between nodes. According to the analysis, the algorithm gives O(n)for both of the time complexity and the space complexity.

The simulation results also demonstrate that our wormhole detection method canachieve a high wormhole detection rate. For the simulation, we organized a wireless sen-


sor network with 100 sensor nodes and deployed up to 40 wormholes in it with differentdensity. In case of a denser network with more wormholes, the detection rate was gettinghigher. In the performance comparison with other detection methods, the proposed TRMgave much bigger detection rate for wormholes with shorter lengths.

In the future, the proposed algorithm is required to enhance the performance for coarsenetworks and consider the separated nodes as well as optimizing the procedure even fordense networks. Performance of the proposed TRM algorithm also should be evaluatedfor various network conditions such as the case that the network has frequent link breaksbetween nodes as a common problem in a practical environment.

Acknowledgments. This research was sponsored in part by the Fundamental Research Funds forthe Central Universities (No. DUT13JS10). This work was also supported in part by the Soonchun-hyang University Research Fund.

References

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2. Akyildiz, W., Sankarasubramaniam, Y., Cayirci, E.: Wireless sensor networks: a survey. Com-puter Networks 38(04), 393–422 (2002)

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9. Jhaveri, R., Patel, D., Jatin, D., Parmar, D., Shah, B.: Manet routing protocols and wormholeattack against aodv. International Journal of Computer Science and Network Security 10(04),12–18 (2010)

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11. Johnson, D., Maltz, D., Broch, J.: Dsr: The dynamic source routing protocol for multi-hop wire-less ad hoc networks. In: Perkins, C. (ed.) Ad Hoc Networks, pp. 139–172. Addison-WesleyLongman Publishing Co., Inc. Boston, MA, USA (2001)

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Guowei Wu received B.E. and Ph.D. degrees from Harbin Engineering University, China,in 1996 and 2003, respectively. He was a Research Fellow at INSA of Lyon, France, fromSeptember 2008 to March 2010. He has been an Associate Professor in School of Soft-ware, Dalian University of Technology (DUT), China, since 2003. Dr. WU has authoredthree books and over 20 scientific papers. His research interests include embedded real-time system, cyber-physical systems (CPS), and wireless sensor networks.

Xiaojie Chen received B.E. and Master degrees from Dalian University of Technology,China, in 2010 and 2013, respectively. He is an engineer in China Unicom. His researchinterests include embedded real-time system, cyber-physical systems (CPS), and wirelesssensor networks.

Yao Lin received B.E. and Master degrees from Harbin Engineering University, China, in1998 and 2001, respectively, and received Ph.D. degree from Dalian University of Tech-nology, China in 2011. She has been a lecturer in School of Software, Dalian University of


Technology (DUT), China, since 2004. She has co-authored one book and over ten scien-tific papers. Her research interests include pervasive computing, cyber-physical systems(CPS), and wireless sensor networks.

Youngjun Lee received B.E. degree from Dept. of Information Security Engineering,Soonchunhyang University, Korea, in 2013. He is currently pursuing his Master degree.His research interests include malware analysis, secure hardware design, and CPS securityand testing.

Kangbin Yim received his B.S., M.S., and Ph.D. from Ajou University, Korea in 1992,1994 and 2001, respectively. He is currently a Full Professor in the Department of Infor-mation Security Engineering and the founding director of the R&BD Center for Securityand Safety Industries (SSI) in Soonchunhyang University. He has served as the executiveboard member of Korea Institute of Information Security and Cryptology, Korean Societyfor Internet Information and The Institute of Electronics Engineers of Korea. He also hasserved as editor of the journals such as JIT, MIS, IJCM, JCPS, JISIS and JoWUA. Hisresearch interests include vulnerability assessment, malware analysis, embedded systemssecurity, and software-hardware co-design and evaluation. Related to these topics, he hasworked on more than fifty research projects and published more than a hundred researchpapers.


The Performance Analysis of Direct/Cooperative Transmission to Support QoS in WLANs

Abstract.

Keywords:

1. Introduction

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