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Optimal Distributed Malware Defense inMobile

Networks with Heterogeneous Devices

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Abstract

From the aspect of malware, since some sophisticated

malware that can bypass the signature detection would

emerge with the development of the defense system, new

defense mechanisms will be required.

At the same time, our work considers the case of OS-

targeting malware. Although most of the current eisting

malware is OS targeted, cross-OS malware will emerge

and propagate in the near future. !ow to efficiently

deploy the defense system with the consideration ofcross-OS malware is another important problem

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Introduction"efense system distribute the optimal signature using special

nodes.

#o deploy an efficient defense system to help infected nodes to

recover and prevent healthy nodes from further infection.

Avoiding whole network unnecessary redundancy using

distribute signatures.

#he efficiency of our defense scheme in reducing the amount of

infected nodes in the system.

Security and authentication mechanisms should be considered.

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$isting System

"evelop a simulation and analytic model for %luetooth

worms, and show that mobility has a significant impact on

the propagation dynamics.

#he former one has the limitations that signature flooding

costs too much and the local view of each node constrains

the global optimal solution.

&ot using design of defence System to detect malware.

'ould not optimally distribute the signatures.

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Proposed System

#o deploy an efficient defense system to help infectednodes to recover and prevent healthy nodes from further

infection.

(ntroduce an optimal distributed solution to efficiently

avoid malware spreading and to help infected nodes torecover.

#o encounter and diffuse the detected malware using

digest algorithm

(t helps us to evaluate the malware free transmission

between nodes even helper nodes are also present

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Message Digest

#he )"* message-digest algorithm is a widely usedcryptographic hash function producing a +-bit +/-

byte0 hash value

)"* is an algorithm that is used to verify data integrity

through the creation of a +-bit message digest fromdata input which may be a message of any length0 that is

claimed to be as unique to that specific data

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Gantt Chart

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System Architecture

Nodecreation

Distributedsignatures

UsingMD5

model

AnalysisMalware

DigestMalware

Performanceevaluation

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Modules

&ode 'reation

!elper 'reation

"istribute Signatures)alware encounter and "igest

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Node reation!

'reate a mobile networks including a number of nodes.

First defined number of nodes and also defined source node,

destination node, intermediate nodes.

#he network contains heterogeneous devices as nodes.

)obile nodes are more efficient to disseminate content and

information in the network.

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Helper Node "ormation!

!elper nodes are referred to as special nodes.

#his node is used to focusing the all nodes.

!elper node is intermediate node for every nodes in the

network.

File can be transmit from source node to destination node

through the help of helpers node

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Distribute Signatures!

#his module is used to analy1ing the malware nodes through

passing the signatures.

#his signatures distributed for every intermediate node from

source node to destination node with the help of the special

node.

#he special node is the helper node. !elper node distribute thesignatures for every intermediate nodes based on the file

contents key will be generated.

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Malware #ncounter and Digest Malwares!

"etect the malware with the help of a content based signatures.

$ponential parameter obtained from the contact records

between helpers and general nodes.

$very intermediate node receive the signatures from helper

node and which intermediate nodes receiving the signatures

twice.

#his time to detecting the malware spreading nodes and

recovering the infected nodes.

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#he )"* message-digest algorithm is a widely usedcryptographic hash function producing a +-bit +/-byte0 hash

value

)"* is an algorithm that is used to verify data integrity through

the creation of a +-bit message digest from data input whichmay be a message of any length0 that is claimed to be as unique

to that specific data

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$% &dentity'(ased Aggregate Signatures

#he main motivation of aggregate signatures is compactness.

!owever, while the aggregate signature itself may be compact,

aggregate signature verification might require potentially lengthy

additional information namely, the at most0 n distinct signer public

keys and the at most0 n distinct messages being signed.

#his paper initiates a line of research whose ultimate ob2ective is to

find a signature scheme in which the total information needed to

verify is minimi1ed.

(n particular, the verification information should preferably be as

close as possible to the theoretical minimum3 the compleity of

describing which signers0 signed what messages0.

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,% Secure "riend Discovery in Mobile

Social Networks

First, they identify a range of potential attacks against friend discovery by

analy1ing real traces.

Second, they develop a novel solution for secure proimity estimation,

which allows users to identify potential friends by computing socialproimity in a privacy-preserving manner.

A distinctive feature of their solution is that it provides both privacy and

verifiability, which are frequently at odds in secure multiparty computation.

#hird, they demonstrate the feasibility and effectiveness of their approaches

using real implementation on smartphones and show it is efficient in terms

of both computation time and power consumption.