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Securing Password For Wireless Device Using
Training Functions of MATLAB
Dr Menal
Dept. of Computer Science
Maharaja Surajmal Institute (GGSIPU)
Janakpuri, Delhi, India
E-mail: menaldahiya@gmail.com
Dr Sumeet Gill
Dept. of Mathematics
Maharshi Dayanand University
Rohtak, Haryana, India
E-mail: drsumeetgill@gmail.com
Abstract— Traditional methods of authentication in wireless
networking use various approaches like storing the passwords in
encrypted form, salting the password, etc... All these simple
approaches are not adequate for the proper functioning of any
organization. Wireless security needs advanced technologies for
protecting data and valuable information. Only cryptographic
methods are not sufficient, wireless community requires more
attention. Artificial Neural Network has the ability to deal with
these types of problems. We propose an authentication
mechanism for wireless equipments used in wireless
communication. Techniques of Artificial Intelligence and Neural
Network have been widely used for solving the problems. This
chapter describes the role of different training functions of the
algorithm in the memorization of the network parameters and
Back propagation algorithm is applied to the collected data. The
proposed method uses neural network concepts for storing the
passwords and authentication process.
Keywords—Authentication; Artificial Neural Network; Back
propagation Algorithm; Wireless Communication; Wireless
Security.
I. INTRODUCTION
This Earlier computer networks were only used by the
university researchers, corporate employees for sending office
information through e-mail or by sharing peripheral devices
among the employees in the military and in government
operations. At that time no one realized the security of data,
because the use of data was limited and by limited persons in a
given amount of time. So, security did not get a lot of attention
that time. But for the last few decades, the existence of
computer network spread widely. Daily, millions of ordinary
people use the network facilities for their convenience such as
for banking, shopping, ticket booking, watching movies
online, chatting, sending emails and for the use of social
networking like Face book, twitter etc. Usage of wireless
devices like mobile phones, smart phones, PDA, Laptops
increases day by day in every field of life. Mobile phones,
smart phones and laptops transfer information and use mobile
data over the wireless networks [1]. Wireless devices have
become the soul of technology and heart of the growing
market. They overcome the market of IT sector and plays a
major role in communication. People use each and every
facility of internet without knowing the security aspects.
The key element for all the organizations or for any system
in the networking is the security of the resources, data and
sensitive information. Public can access the data from
anywhere, anytime by making hotspots without knowing the
security measures. The intruders easily attack on such type of
users and misuse their sensitive information very often. Due to
the proliferation of the technologies of wireless networking,
around 80% of the society uses the facility of wireless
networking. The rapid advent of the internet and the
emergence of various World Wide Web applications across
the world, organizations generate a large amount of data on a
daily basis. Different types of information and sensitive data is
shared between the organizations on a regular basis. Data
security is the utmost critical issue in transmission over
Wireless Network. Network security issues are also important
as layman moving towards digital information age. Network
security is comprised of both public and private networks that
include businesses, government agencies and individuals. In
other words, Computer security is a vast topic and it includes
data security and network security [2].
Before the widespread use of the internet, the security of
information or data for an organization was provided to the
managers or other reliable post and administrative means.
Gradually, rapid advancement in the computer world requires
automated devices to protect data and important information
collected and put aside in the system turn into an evident
basically in sharing systems. The general name given by the
researchers to a set of tools that safeguard from hackers called
computer security [3]. Security covers vast subject matter
which includes data security and network security. Security
International Journal of Pure and Applied MathematicsVolume 118 No. 7 2018, 9-19ISSN: 1311-8080 (printed version); ISSN: 1314-3395 (on-line version)url: http://www.ijpam.euSpecial Issue ijpam.eu
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more bothered to ensure that unwanted user don’t access or
alter the message destined for specific receivers and
unauthorized people are not trying to access the services [4].
Network security is a concept to protect data transmission over
a wired or wireless network. The easiest approach to safeguard
a network device from unauthorized access is to assign him a
different user id with a matching password [5]. Network
security is the main pillar in information security as it is
responsible for the whole information passes through the
network that includes hardware and software security
functions, access control, features and operating procedures,
etc... Cryptography is a technology that is introduced to
protect the network from threats [6]. Business Houses and
companies started protecting their useful systems and
networks using cryptographic techniques as a result of prompt
advancement in the global use of computerized data
collection, advancement in communications and
eavesdropping technologies. Earlier, the cryptographic
techniques used exclusively by the military and government
communities. Later on, it is necessary to use some
cryptographic methods or some other means of protection to
enhance the security. Computer systems and networks which are stored,
processing and communicating sensitive or valuable information require protection against such unauthorized access [7]. The usage of the technology and advancement in the technology is directly related to the ascending graph of the wireless networking. However, side by side intruders and unauthorized persons will also try to breach the security measures. This leads an increasing need for taking extra security measures in wireless technology. There are many solutions and methods that provide security to the wireless communication [8]. Network security considers authorization of access of data in a network. Password authentication is one of them. Every user assigns an Id and corresponding password that allows them to access information without any problem. This Id is the identity of its authorized usage
II. AUTHENTICATION MECHANISM
Authentication is a two way process in which user
confirms his or her identity to the computer system [9].
Authentication schemes are mostly based on passwords, smart
cards and biometrics. Authentication ensures that the services
and system resources are used by the authentic person.
Authentication is of two types- message authentication and
user authentication. In message authentication, when the
sender sends the message, it is not necessary that the receiver
is online. On the other hand, in user authentication both, the
receiver and the sender must be present in real time
communication. User authentication involves two important
entities claimant and the verifier [10]. The user whose
distinction requisites to be established is called claimant and a
verifier, who attempt to establish the distinction of the
claimant. In user authentication, always claimant proves his
identity to the verifier.
A. Authentication Methods
There are different methods for verifying the identity, such as- some are we known as a pin, password, keys and some we possess as Identification card, smart card, etc. or some are inherent as fingerprint, handwriting, etc... Currently authentication methods are categorized into [11].
The Biometric is a study of methods that distinguish uniquely between physical characteristics and personal traits of an individual [12]. For example face recognition, iris recognition, handwriting and voice, etc... Two methods applied to authenticate users in biometric authentication are human physiological and behavioral characteristics. Biometric features are not temporary and are not manipulative easily. There is no chance of biometric objects lost, stolen, forgotten or any other mishappening. This is an advantageous feature of biometrics for clients as well as for the organization. Having these advantages, there is disadvantages too [13]. Biometric method is still in improving state as the performance of biometric systems is not ideal. People who are not physically fit or having some problem with their body parts are not suitable for biometric authentication systems. Although, biometric systems are not perfect they also show errors sometimes. As in the case of fingerprint authentication, there are chances of an authorized individual can be denied access [14]. The high cost of hardware and software installation is the major fall in the use of this system. The physical characteristics of an individual changes when he/she aged or affected by some physical injuries. Besides all the drawbacks biometric systems are very desirable also, the most attractive feature is data can’t land up like missing, taken or forgotten as other authentication mechanism.
This mechanism includes password and PIN methods. Password authentication is the most popular method, as it is the old one, but since many of us are interacting with our systems through this method. A user account and the corresponding password are assigned to the account [15]. Whenever the user enters the correct password, the system verifies the user’s authentication from a database that stores all the information regarding the account holders and their password. PIN’s are also working in the same manner and popularly used in smart cards, ATM cards, etc... They use strong security mechanisms like cryptography, recognition, etc. [16].
This technique contain both text based and image based passwords. Most of the researchers used knowledge based authentication techniques with token based methods for increasing the security level of the user and the system [17].
B. Password Based Authentication Scheme
Different authentication mechanisms have their own
disadvantages and advantages regarding security, reliability
and support to the system. Password based authentication is
the simplest and oldest method of authentication [18]. Keeping
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passwords are very convenient for users, they are easy to
implement, popular and inexpensive. Other authentication
schemes are also available like smart card and biometrics, but
password authentication is widely used by the public. In
password based authentication, the user is the claimant and
password is something that he knows [19]. Password is
necessary when he tries to access a system to use the services
provided by the system. Password based authentication uses
various schemes like:
First Password Based Authentication Scheme where a file is stored in the system in which user identification and respective passwords are sorted [20]. Whenever a user wants to access the system resources or any service he or she sends his or her user identification and password in plain text. The system, who acts as verifier uses user identification for searching the password in the file. If the sent password matches with the password has already stored in the file, access is granted otherwise rejected.
Second Password based authentication scheme where
system uses more secure approach to store the
password. The passwords that are sent by the users in
plain text are easy to hack by the intruders, so the file
become insecure. In this approach the hash function is
applied to the password, which is almost impossible to
guess the value of the password. Hash functions are
one way encryption methods [21, 22]. It acts like a
summary of a message and used for data integrity
purpose i.e. the message which has been sent by the
sender is received by the receiver without any
modification in the message, contents remains same
throughout the transmission. These are various
algorithms to create hash of a message such as MD5 or
SHA-1. Message digests for the same original data
should always be same. The important property of the
message digest is that it should not work in the
opposite direction and the message digest of two
different messages must be different [23]. In the first
approach, the user password is stored as it is in a
password file without any encryption. But the second
approach is different from the previous one, where user
password is stored in the encrypted form or in the value
that is derived from the password using different
algorithms. That derived value is stored as a password
in the database. When a user needs to be authenticated,
he or she must login to the system by its user Id and
password. After this, a system derived message digest
from the received password and sends both user id and
derived password to the server for authentication
purpose [24]. When the server receives the pair of user
id and corresponding password, the user authentication
program checks it and validates the combination
against the combination that is already stored in the
database and returns as acknowledgement according to
the result. If the combination of user id and derived
password matches than it grants access to the user and
sends positive acknowledgement otherwise it denied
the access. In this scheme, using hash functions in
password completely safe and secure because of the
process of derivation of the value of hashed password
from the original password must not provide idea about
the original password conditions, Identical hash value
is derived by the algorithm every time for the same
password, It should not be feasible for an intruder to
guess the password and obtain the correct derived
password [25].
Salting Approach is another password based
authentication scheme, in which hash function is again
used, but after a random string called salt. This random
string attached to the password, and then the salted
password is hashed. In first approach only user id and
password is stored in the database. In the second
approach, user id and hashed password are stored in the
database. Now in third approach, the user id, the salt
and the hashed password are then stored in the database
file. Now when a person wish to access the properties
of the device, then the system extracts the salt,
concatenates it with the received password and then
derived hash function of the result and compares it with
the hash value stored in the file [26]. If the values
matches, then system resources are accessed but the
user otherwise it is not an authorized person for the
system. These entire password based authentication
schemes are good, but not secure at all. As
authentication schemes increasing the level of security,
parallel hackers also take out the solution of breaching
the security of an organization or system. Attacks on
first approach is Eavesdropping, Stealing a password,
Accessing a password file, Password stored in plain
text, Password travel in plain text from system to
server, Guessing and attack on second approach is
Dictionary attack [27, 28, 29].
III. ARTIFICIAL NEURAL NETWORK
Back-Propagation Network (BPN) is an eminent
subdivision of Artificial Neural Network. The basic system
architecture is a multilayer feed forward network using back
propagation model. The proposed and achieved values of the
neural network are defined as ix as well as hs for ith
neurons
in the input layer along with hth
neuron in the output layer,
including ihw is the weight that link the input of ith
neuron to
the output of hth
neuron [30].
The achieved results are likely by the connection,
h
h yfs 1
Where hy1 are the activation vectors of any layer j and likely
as:
I
j
iih
h xwy1
1
The error formulation initiated for a single perceptron is
normalized to accommodate all squared errors of the achieved
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results j=1, 2… J for a particular design p=1, 2, 3… p that is at
the input as well as comes across the output error.
2
12
1
J
j
jj
p ydE
In which, d is the wished output vector t
Jdddd ]..............[ 321
Weights adopted as below:
hj
p
hjw
Ew
Where, stands for Back Propagation learning rate
Formulation adopted as below:
h
hh
j
h
jj
h
j
Ph
hh
j
p
hj
h
h
j
p
hj
p
ysy
ys
ys
Eys
y
E
w
y
y
E
w
E
...
1
1
1
Since
h
j
h
ij
y
yS
= )(
.h
jj yS
Therefore, weight adaptation is presently designated as below:
h
jh
h
jj
J
j
jj
h
jh
h
jjh
jj
P
ih ysySydysySys
Ew ).(.).(.
.
1
.
The final adjusted weight is given by:
h
jh
h
jj
J
j
jjhjhj ysySydtwtw ).(.)()1(.
1
N
j
j
i
j
hhhj
p
jjjih xySwyydw1
.
).(..
For accessing the resources user login the system with
password. The password can enjoy a sundry kind of statistic
such as series, characters and some alphanumeric data. There
are three main components of the authentication process: input
parameters, authentication algorithm and output values. In
figure 1, we show the simplest authentication method that uses
the password as an input and any encryption algorithm that
encrypts the password and store it and further compare it when
someone login the system. In password-based authentication
approach, the passwords are encrypted by one-way hash
functions or encryption algorithms and then are stored as some
patterns [31, 32]. Nevertheless, mentioned approach has few
drawbacks. An attacker still capable of linking up a forged
pattern or replace someone’s encrypted password. There is other technique for above schemes which usages
neural network to overcome the security problem. This technique stores encrypted passwords by training the network with the help of Back Propagation Algorithms. Here, the system keeps the trained weights and when a system memorizes these weights and stores it in the form of encrypted files. We eliminate this encrypted file through which security of the network improves. However, it provides greater security as to prior mechanisms in figure 2.
Fig. 1. Simple Authentication Method.
Fig. 1. Proposed Authentication Method.
IV. EXPERIMENTAL SETUP
For authentication system, we use the information of a
wireless device, i.e. a mobile phone. The user chooses the
password which can be either in characters, in numeric or in
both. Here, the training model (BPNN) is a supervised
learning model. The model made up of input, hidden and
output layers. Training set of the experiment is shown in table
1. Password is consisting of eight characters and for doing
simulation, it transformed into 8-bit binary code. Therefore,
the BPN architecture has sixty four input nodes in the input
layer, 32 processing neurons in the hidden layer and sixty four
output units in the output layer. The coupling strength of the network is increased by
training of the neural network with a password. Password is taken as an input to the network and when training is completed up to the minimized error values, and then obtained output is the final mapped value of the password.
TABLE I. WIRELESS DEVICE AND ITS PASSWORDS
Wireless
Device
Password Hashed password
Mobile Phone
india47i
2e9effee94253981a7e57ae1bdad069c /
01101001011011100110010001101001
01100001001101000011011101101001
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A. Training Algorithms
There are different methods of Back Propagation Neural
Network that are used to train the network and are applied on
this experiment such as the Gradient Descent Algorithm,
Conjugate Gradient Algorithm and Quasi-Newton Algorithm.
Along with algorithm there are supported training functions
also in the Feed Forward Neural Network that are as follows:
Trainbr (Bayesian Regularization), Traingdx (Gradient
Descent with adaptive learning rate BP), Traingdm (Gradient
Descent with momentum BP), Trainlm (Levenberg-Marquardt
BP), Trainscg (Scaled Conjugate Gradient BP), Traincgf
(Fletcher-Powell Conjugate Gradient BP), Traincgp (Polak-
Ribiere Conjugate Gradient BP), Trainbfg (BFGS Quasi-
Newton BP), Traingd (Gradient Descent BP), Trainrp
(Resilient Back Propagation) [33].
V. TRAINING USING NEURAL NETWORK FUNCTIONS
A. Training Using Trainlm
TABLE II. THE PARAMETERS USED FOR TRAINING BY NETWORK USING
THE BACK PROPAGATION MODEL.
Parameter Value
Neurons in Input Layer 64
Number of Hidden Layers 1
Neurons in Hidden Layer 32
Neurons in Output Layer 64
Training Function Trainlm
Time 2 Sec
Minimum Error Exist in the Network 0.001
Error Percentage 0
Initial Weights and Biased Term Value
Values between 0 to 1
0.090 0.028 0.025 -0.07 0.015 0.030 0.024 0.031
0.053
0.035 0.055 -0.06 0.033 -0.03 -0.09 0.021
0.909
0.016 -0.05 0.003 0.045 -0.07 -0.03 0.042
0.001
-0.04 -0.09 -0.05 0.005 0.030 -0.06 0.967
Weights between Layer 1 to Input Layer
0.090 -0.08 0.025 -0.91 0.909 0.003 0.036 0.006
0.003
-0.05 -0.02 -0.06 0.033 -0.03 -0.02 0.031
0.008
0.018 0.995 0.903 0.009 -0.98 -0.01 0.082
0.001
-0.04 0.079 -0.04 0.065 0.006 -0.90 0.903
Weights between Hidden Layer to Output Layer
GRAPH 1. PERFORMANCE PLOT OF NETWORK WITH FUNCTION TRAINLM.
B. Training Using Traincgp
TABLE III. THE PARAMETERS USED FOR TRAINING BY NETWORK USING
THE BACK PROPAGATION MODEL.
Parameter Value
Neurons in Input Layer 64
Number of Hidden Layers 1
Neurons in Hidden Layer 32
Neurons in Output Layer 64
Training Function Traincgp
Time 1 Sec
Minimum Error Exist in the Network 0.001
Error Percentage 0
Initial Weights and Biased Term
Value
Values between 0 to 1
0.003 -0.03 0.065 -0.07 0.019 0.034 0.074 0.054
0.904
0.912 0.075 -0.06 -0.03 -0.93 -0.99 0.921
0.008
-0.06 -0.08 0.977 0.007 -0.97 -0.93 0.942
0.034
-0.08 -0.09 -0.02 -0.05 0.055 -0.04 0.067
Weights between Layer 1 to Input Layer
0.044 -0.08 0.005 -0.91 0.900 0.006 0.038 0.907
0.954
-0.05 -0.02 -0.06 0.073 -0.05 -0.02 0.933
0.006
0.013 -0.95 0.973 0.001 -0.07 -0.07 0.988
0.009
-0.04 -0.09 -0.09 0.035 0.004 -0.96 0.088
Weights between Hidden Layer to Output Layer
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GRAPH 2. PERFORMANCE PLOT OF NETWORK WITH FUNCTION TRAINCGP.
C. Training Using Traingdm
TABLE IV. THE PARAMETERS USED FOR TRAINING BY NETWORK USING
THE BACK PROPAGATION MODEL.
Parameter Value
Neurons in Input Layer 64
Number of Hidden Layers 1
Neurons in Hidden Layer 32
Neurons in Output Layer 64
Training Function Traingdm
Time 17 Sec
Minimum Error Exist in the Network 0.001
Error Percentage 0
Initial Weight and Bias Term Value Values between 0 to 1
0.070
0.013 0.006 0.031 0.042 0.916 0.002 0.063
0.921
0.045 -0.07 -0.01 0.973 -0.08 0.987 0.921
0.038
0.950 0.034 -0.03 0.033 0.028 0.005 -0.01
0.025
0.005 0.914 -0.02 0.026 0.988 0.002 0.942
Weights between Layer 1 to Input Layer
0.003
0.991 0.023 0.092 0.052 0.942 0.962 0.934
0.982
0.962 0.972 -0.02 -0.02 0.972 -0.02 0.085
-0.08
0.075 -0.02 0.982 0.932 0.942 -0.04 0.031
-0.06
0.945 -0.06 0.032 0.982 0.989 0.074 0.073
Weights between Hidden Layer to Output Layer
GRAPH 3. PERFORMANCE PLOT OF NETWORK WITH FUNCTION TRAINGDM.
D. Training Using Trainscg
TABLE V. THE PARAMETERS USED FOR TRAINING BY NETWORK USING
THE BACK PROPAGATION MODEL.
Parameter Value
Neurons in Input Layer 64
Number of Hidden Layers 1
Neurons in Hidden Layer 32
Neurons in Output Layer 64
Training Function Trainscg
Time 2 Sec
Minimum Error Exist in the Network 0.001
Error Percentage 0
Initial Weight and Bias Term Value Values between 0 to 1
0.991
-0.03 0.057 0.986 0.001 0.079 0.018 0.931
0.931
-0.05 -0.05 0.028 0.086 0.037 0.045 0.921
0.021
0.023 0.946 -0.09 -0.01 0.002 -0.05 0.021
0.028
0.003 0.079 0.026 0.994 0.976 0.043 0.913
Weights between Layer 1 to Input Layer
0.993
-0.02 0.068 0.068 0.932 0.032 -0.08 0.090
0.092
0.002 0.902 0.031 -0.03 -0.07 0.932 0.004
0.922
-0.05 0.042 -0.92 0.021 0.976 -0.01 0.023
0.045
-0.06 0.055 0.949 0.045 -0.05 0.945 0.987
Weights between Hidden Layer to Output Layer
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GRAPH 4. PERFORMANCE PLOT OF NETWORK WITH FUNCTION TRAINSCG.
E. Training Using Trainsrp
TABLE VI. THE PARAMETERS USED FOR TRAINING BY NETWORK USING
THE BACK PROPAGATION MODEL.
Parameter Value
Neurons in Input Layer 64
Number of Hidden Layers 1
Neurons in Hidden Layer 32
Neurons in Output Layer 64
Training Function Trainrp
Time 1 Sec
Minimum Error Exist in the Network 0.001
Error Percentage 0
Initial Weight and Bias Term Value Values between 0 to 1
0.010
-0.07 0.061 -0.02 0.088 0.965 0.077 0.090
-0.04
0.970 0.029 -0.01 0.083 0.051 -0.04 -0.05
-0.01
0.935 0.925 -0.04 0.048 -0.02 0.053 0.909
0.934
0.047 0.091 0.989 0.080 -0.07 -0.03 0.055
Weights between Layer 1 to Input Layer
0.993
-0.03 -0.08 0.048 0.032 0.902 -0.06 0.006
0.992
-0.02 0.902 0.031 -0.03 -0.07 0.932 -0.05
0.022
-0.92 0.042 0.002 0.921 0.976 -0.01 0.999
0.045
-0.95 0.055 0.949 -0.04 -0.05 0.945 0.045
Weights between Hidden Layer to Output Layer
GRAPH 5. PERFORMANCE PLOT OF NETWORK WITH FUNCTION TRAINRP.
F. Training Using Trainbfg
TABLE VII. THE PARAMETERS USED FOR TRAINING BY NETWORK USING
THE BACK PROPAGATION MODEL.
Parameter Value
Neurons in Input Layer 64
Number of Hidden Layers 1
Neurons in Hidden Layer 32
Neurons in Output Layer 64
Training Function Trainbfg
Time 0 Sec
Minimum Error Exist in the Network 0.001
Error Percentage 0
Initial Weight and Bias Term Value Values between 0 to 1
0.002
0.977 0.917 -0.01 -0.06 0.008 0.082 0.084
0.001
0.083 0.025 0.032 -0.02 0.994 0.934 0.014
0.037
-0.07 0.985 -0.05 -0.02 -0.03 0.055 0.021
0.944
-0.03 0.064 0.986 0.025 -0.07 0.948 0.913
Weights between Layer 1 to Input Layer
0.025
-0.90 0.909 0.005 -0.91 0.900 0.048 0.032
-0.03
-0.06 -0.03 -0.02 -0.06 -0.07 -0.03 -0.02
-0.99
0.93 0.009 -0.95 0.973 0.001 -0.04 0.921
-0.07
0.040 -0.05 -0.09 -0.09 -0.03 0.949 0.045
Weights between Hidden Layer to Output Layer
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GRAPH 6. PERFORMANCE PLOT OF NETWORK WITH FUNCTION TRAINBFG.
VI. RESULT AND DISCUSSION
Multiple experiments were performed on a feed forward
Neural Network by taking different training functions.
Different training functions have different properties, which
work accordingly for the topology of the network and give
results. Graph 1 to graph 6, depicts the network performance
by various training functions. Training parameters that were
used in the process of training a network are summarized in
tables from table 2 to table 7.
For performing an experiment, we used topology of {64-
32-64} indicates 64 input neurons, 32 hidden neurons and 64
output neurons [34, 35]. Also, six back propagation methods
that are Levenberg-Marquardt (LM), Scaled Conjugate
Gradient (SCG), Gradient Descent with Momentum (GDM),
Conjugate Gradient Back propagation with Polak-Riebre
Updates (CGP), Resilient Back propagation (RP) and Quasi-
Newton (BFGS). If we compare the performance of different
networks, according to the training functions then the results
are:
A. On The Basis of Epochs
TABLE VIII. COMPARISON OF DIFFERENT TRAINING FUNCTION BASED ON
EPOCHS.
S.No. Training Function No. Of Epochs
1 Trainlm 8
2 Trainbfg 6
3 Traingdm 1000
4 Trainscg 14
5 Traincgp 5
6 Trainrp 59
The above summary of training functions with their
corresponding epochs in table 8 indicates that trainlm, trainbfg
and trainrp show equivalent results. Originally epoch is set to
1000. Training function, traingdm takes 1000 iterations to the
network with a given topology while another gradient descent
method takes 59 epochs. Trainscg and traincgp functions train
the network in less number of epochs as compared to gradient
descent methods while trainlm and trainbfg shows better
results.
Multiple experiments were performed on a feed forward
Neural Network by taking different training functions.
Different training functions have different properties, which
work accordingly for the topology of the network and give
results. Graph 1 to graph 6, depicts the network performance
by various training functions. Training parameters that were
used in the process of training a network are summarized in
tables from table 2 to table 7.
B. On The Basis of Epochs
TABLE IX. COMPARISON OF DIFFERENT TRAINING FUNCTION BASED ON
MSE.
S.No. Training Function MSE
1 Trainlm 0.785
2 Trainbfg 0.480
3 Traingdm 0.216
4 Trainscg 0.790
5 Traincgp 2.860
6 Trainrp 4.690
The Mean Square Error (MSE) values in table 9 refer to
the state where it terminates the training of all the Back
Propagation Algorithms. The maximum error is shown by
trainrp function and traincgp function. Trainscg and trainlm
shows the neckline difference on the other hand, training
functions trainbfg and traingdm shows better performance.
C. On The Basis of Time
TABLE X. COMPARISON OF DIFFERENT TRAINING FUNCTION BASED ON
TIME.
S.No. Training Function Time
1 Trainlm 2sec
2 Trainbfg 0 sec
3 Traingdm 17sec
4 Trainscg 2sec
5 Traincgp 1sec
6 Trainrp 1sec
Comparison of different training functions on the basis of
time in table 4.10 shows that traingdm takes much time to
train the network as compare to other training functions.
Trainlm, trainscg, traincgp and trainrp train the network in few
seconds while trainbfg takes negligible time.
The above computations and evaluations describes that
each training function has its own performance criteria. Some
trains the network in time, but take much iteration and
performance is not up to the mark while some training
functions takes time and train the network fully.
VII. CONCLUSION
From the above computed results and discussion of all the
points, we conclude that for the authentication of any wireless
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16
equipment in the networking training functions plays an
important role. Database administrators or system analysts
apply any training function for memorizing the pattern sets in
the memory. The performances of different training functions
according to different aspects summarizes in the previous
section. We can apply one of the functions of the back
propagation algorithm for getting better result. Techniques of
Artificial Intelligent and Neural Network have been used
broadly for solving the problems related to pattern recognition
and classification, signal processing and optimization, etc.
Authentication is mainly focused on the use of passwords,
smart cards and biometrics. Password based authentication is
very common, but due to some limitations like dictionary
attacks and long searching process, researchers have been
working continuously to find out the solution. R.C. Merkle
proposed the scheme of encryption of password by using hash
function and then stored in the table. Later on, verification
table is utilized to replace password tables. Chien et al.
proposed password authentication scheme using smart cards
and prevent the reflection attack and insider attack. Today in
every field, Neural Network has been used to overcome the
traditional approaches. Involvement of Neural Network in the
intrusion detection field becomes apparent when one views the
intrusion detection problem as a pattern classification
problem. Hwang et al. proposed method that produces hashed
password according to input file and replace the password
table using back propagation algorithm. Another engineer
Reyhani et al. proposed a method in which they train a neural
network to store encrypted passwords using RBP algorithm.
Here, in this chapter, we implement back propagation
algorithm on the user’s passwords. We train the passwords
and store it in the form of network parameters by using
different training functions. These functions affect the speed,
convergence rate, space and time of the system. Back
propagation method of neural network is a supervised feed
forward learning algorithm which is applied in the experiment.
Different training functions were applied to the network
architecture. We applied two methods from each of the three
algorithms for performing the simulation. Newton’s method
gives better and faster optimization results as compared to
gradient descent and conjugate gradient descent algorithms.
Both methods of Newton’s algorithm converge the network at
a faster rate and produces good results according to different
criteria such as mean square error, number of epochs and time
of training. Our proposed system is applicable in an open
environment and helps in intrusion detection.
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