Design and implementation of network security using genetic algorithm

IJRET: International Journal of Research in Engineering and Technology ISSN: 2319-1163
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Volume: 02 Issue: 02 | Feb-2013, Available @ http://www.ijret.org 172
DESIGN AND IMPLEMENTATION OF NETWORK
SECURITY USING GENETIC ALGORITHM
Soumya Paul1
, Inadyuti Dutt2
, S.N. Chaudhuri3
1
Associate Professor & Head, 2
Asst. Professor, Dept. of Computer Application, B.P. Poddar Institute of Management &
Technology, West Bengal, India, 3
Director, Kanad Institute of Engineering & Management, West Bengal, India,
soumya.paul2000@gmail.com, inadyuti&gmail.com, satya.chaudhuri@rediffmail.com
Abstract
Over the last few years, Secured transmission of data has been a major issue in data communication. This project mainly concerns
about the security of confidential information and data transmission using public key cryptography with Genetic Algorithm in order
to provide confidentiality, authentication, integrity and non-repudiation of the messages. First, an algorithm is developed and
implemented to generate a key pair (Private and public Key).A plain text is encrypted using the Public Key of receiver to produce an
intermediate cipher. The intermediate cipher is again encrypted using genetic algorithm to produce final cipher. The final cipher
first decrypted to produce the intermediate cipher which in turn decrypted to get the plain text using the Private key of the
receiver or vice versa.
Index Terms: Network Security, Genetic Algorithm
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1. INTRODUCTION
1.1 Network Security:
Network security consists of the provisions and policies to
prevent and monitor unauthorized access, misuse,
modification, or denial of a computer network and network-
accessible resources. Network security involves the
authorization of access to data in a network, which is
controlled by the network administrator. Users choose or are
assigned a password or other authenticating information that
allows them access to information and programs within their
authority. Network security covers a variety of computer
networks, both public and private, that are used in everyday
jobs conducting transactions and communications among
businesses, government agencies and individuals.
1.2 Genetic Algorithm:
Genetic algorithms are inspired by Darwin's theory about
evolution. Solution to a problem solved by genetic algorithms
is evolved.
Algorithm is started with a set of solutions (represented by
chromosomes) called population. Solutions from one
population are taken and used to form a new population. This
is motivated by a hope, that the new population will be better
than the old one. Solutions which are selected to form new
solutions (offspring) are selected according to their fitness -
the more suitable they are the more chances they have to
reproduce.
This is repeated until some condition (for example number of
populations or improvement of the best solution) is satisfied.
1.3 Outline of Remaining Section:
The paper is organized as follows Section 2 states the security
of files that contain certain confidential information. The
detail description of the proposed encryption decryption
algorithms with illustration of examples are explained in
section 3. Finally, the paper concludes in section 4.
2. PROBLEM STATEMENT
Encryption method follows as: A pair of key (Public and
Private) generated. In Asymmetric-key cryptography, it is
obvious that when a text is encrypted with a public key, it is
decrypted with corresponding private key and vice-versa. The
plain text is encrypted using public key to produce
Intermediate cipher. The Intermediate cipher is further
encrypted using Genetic Algorithm to produce the final cipher.
Decryption method: The Final cipher is decrypted using
Genetic Algorithm to get intermediate cipher which is again
decrypted using corresponding private key to get the plain
text.
Initial Encryption:
Intermediate cipher = Encrypt (plaintext, public Key) OR
Intermediate cipher = Encrypt (plaintext, Private Key)
Genetic Encryption:
Final cipher = Encrypt (Intermediate cipher)

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Genetic Decryption:
Intermediate cipher = decrypt (Final cipher) Final Decryption:
Plaintext = decrypt (Intermediate cipher, Private Key) OR
Plaintext = decrypt (Intermediate cipher, Public Key)
3. PROPOSED ALGORITHM
3.1 Proposed Key Generation Heuristic:
Step 1: Generate two non coprime number (a, b)
Step 2: Set l: =L.C.M (a, b) g: = G.C.D (a, b)
Step 3: Set (a, g) as private key
Step 4: Set x: = ((a-1)/g) + ((a-1) %g)
y: =g
Step 5: Store x, y
Step 6: Set (b, l) as public key
Step 7: Set p: = ((l-1)/b + ((l-1) %b)
q: =b Step 8: Set p, q Step 9: Stop
3.2.1 Proposed Encryption Heuristic Using
Function:-
Input : text, key type, public key (p, q)
or private key (x, y)
Output : Intermediate Cipher
Step 1: if key type= private key
Read public key (p, q) from database
Set m: = 2 else
Read private key (x, y) from database
Set m: = 1
Step 2: Set x: = ((x-(y-1))*y) +y
Step 3: Set p: = ((p-(q-1))*q) +q
Step 4: if (x/y=p/q) and (x*q=p*y) then
Set n: = x*q
Step 5: Set key_arr []: =n
Step 6: if (length of text = odd number) Then text: =text + @
Step 7: Set i: =0, c: =” ”, j: =0
Step 8: while (i<length of text) Repeat Step 9 to Step 12
Step 9: if i= EVEN number
Set c: =c + text [i] + (m* key_arr[j]) Else
Step 10: Set c: = c+text[i] - (m* key_arr[j]) End if
Step 11: Set j: = j+1
Step 12: if j=key_length then set j: =0
Step 13: Print c
Step 14: Stop
3.2.2 Proposed Encryption Heuristic Using
Genetic Algorithm:-
Input : Intermediate Cipher(c)
Output : Final Cipher
Step 1: Set st1:= substring of c (1 to c/2)
step2:= substring of c (c/2 to c) Step 2: Set i=1
Step 3: while (i<length of text) Repeat Step 4 to Step 8
Step 4: perform crossover at mate point i with st1 (0, i) & st2
(i, n)
& st2 (0, i) & st1 (i, n)
Step 5: Set st: = st1+Reverse (st2)
Step 6: Reverse st & set gst= st
Step 7: compare c & gst to find out fit value (no of character
position remain unchanged)
Step 8: select the gst having minimum no of fit value
Step 9: Return gst
Step 10: Stop
3.3 Proposed Decryption heuristic using
Genetic Algorithm:-
Input : Final Cipher(c)
Output : Intermediate Cipher
Step 1: Set st: = reverse (cipher)
Step 2: Set n=length of cipher
Step 3: Set st1:= substring of c (1 to n/2)
st2:= substring of c (n/2 to n)
Step 4: Set st2: =Reverse of st2
i:=1
Step 5: while (i<length of st2) Repeat Step 6 to Step 9
Step 6: perform crossover at mate point i
with st1 (0, i) & st2 (i, n)
and st2 (0, i) & st1 (i, n)
Step 7: Set gst: =st1+st2
Step 8: compare gst & cipher to find out fit value
(no of character position
Remain unchanged)
Step 9: select the gst having minimum no of fit value
Step 10: Return gst
Step 11: Stop
3.4 Proposed Decryption Heuristic using
Function:-
Input : Intermediate cipher, keytype, public
key
(p, q) or private key (x, y)
Output : Plain Text
Step 1: if key type= public key
Set m: = 2 & Read private key (x, y) from database
else
Set m: = 1 & Read public key (p,q) from database
Step 2: Set x: = ((x-(y-1))*y) +y
Step 3: Set p: = ((p-(q-1))*q) +q

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N l u h u a
u i u ] ! F
N i u h u a
u l u ] ! F
N i u h u a
u l u ] ! F
Step 4: if (x/y=p/q) and (x*q=p*y) then
Set n: = x*q
Step 5: Set key_arr [] : =n
Step 6: Set i=0, c=” ”, j=0
Step 7: while (i<length of plmcipher) Repeat Step 4 to Step 7
Step 8: if i= EVEN number then
Set c: =c + plmcipher[i] - (m* key[j]) Else
Step 9: Set c: = c+plmsiphert[i]+(m* key[j]) End if
Step 10: Set j: = j+1
Step 11: if j=length of key [] then Set j: =0
Step 12: Print c
3.5 Proposed Cryptographic Algorithm:
Step 1: Start
Step 2: Call Proposed Encryption Heuristic
Step 3: Call Proposed Encryption Heuristic using
Genetic Algorithm
Step 4: Call Proposed Encryption Heuristic using
Genetic Algorihm
Step 5: Call Proposed Decryption Heuristic
Step 6: Stop
3.6 Illustration of Proposed Algorithm with Example:
:
A. Key Generation
Two Non-Co prime number generated: a=39, b=453
l:=L.C.M (a, b)=L.C.M(39,453)=5889 g:= G.C.D (a,
b)=G.C.D(39,453)=3
x: = ((a-1)/g) + ((a-1) %g)
=((39-1)/3)+((39-1)%3)=14 y: =g=3
p: = ((l-1)/b + ((l-1) %b)
=((5889-1)/453+((5889-1)%453)=464 q: =b=453
Publish (x,y) as private key and (p,q) and public key
B. Encryption
STEP 1: Encryption using Public Key:
Input Public key(p,q)=(464,453) Input plain text: “Good
Morning”
Read Private Key(x,y)=(14,3) from Database x: = ((x-(y-
1))*y) +y=((14-(3-1))*3)+3=39
p: = ((p-(q-1))*q) +q
=((464-(453-1))*453)+453=5889 (x/y=p/q=13) and
(x*q=p*y=17667)
n: = x*q=39*453=17667 key_arr[]={7,6,6,7,1} m:=1 and c=””
when i=1 ie, ODD
c: =c + ASCII(text [i])+ (m* key_arr[j])
=ASCII(„G‟)+(1*7)=N
when i=2 ie, EVEN
c: = c+ ASCII(text[i])- (m* key_arr[j])
= N+(ASCII(„o‟)-(1*6))=Ni
Finally we get the intermediate cipher :
Niu]!Fuluhua
STEP 3: Encryption using Genetic Algorithm
Input: Intermediate Cipher (st): Niu]!Fuluhua n:=length of st
st1:=substring(0,n/2)= Niu]!F
st2:=substring(n/2,n)= uluhua
Iteration 1: Cross Over
Mutation: NluhuaF!]uiu
Inversion(gst): u i u ] ! F a u h u l N (st)
:N i u ] ! F u l u h u a
Fit value: Comparing st and gst to find out no. of characters
whose position remained unchanged.
fit(st,gst)=5
N I u ] ! F
u l u h u a
=
:
Mutation: NiuhuaF!]ulu
Inversion(gst): u l u ] ! F a u h u i N (st)
:N i u ] ! F u l u h u a
fit(st,gst)=4
N i U ] ! F
u l U h u a
=
Mutation: NiuhuaF!]ulu
Inversion(gst): u l u ] ! F a u h u i N (st): N i u ] ! F u l u h u a
fit(st,gst)=4

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N i u ] ! a
u l u h u F
N l u h u F
u i u ] ! a
N i u ] u a
u l u h ! F
N i u ] ! a
u l u h u F
N i u ] ! a
u l u h u F
U l u h u a
N i u ] ! F
u l u h u a
=
whose position remained unchanged. fit(st,gst)=2
So, fittest gst having minimum fit value is : u l u h u
F a ! ] u i N
C. Decryption
STEP 1: Decryption using Genetic
Algorithm
Input: Final Cipher (st):
u l u h u F a ! ] u i N Inversion: N i u ] ! a F u h u l u
Mutation(mst): N i u ] ! a u l u h u F
n:=length of st st1:=substring(0,n/2)= N i u ] ! a
st2:=substring(n/2,n)= u l u h u F
Iteration 1: Crossover
N i u ] ! a
u l u h u F
=
Mutation: Niu]uaF!hulu
Inversion(gst): u l u h ! F a u ] u i N
(st):N i u ] ! F u l u h u a
gst= N l u h u F u i u ] ! a
st= u l u h u F a ! ] u i N
gst= N i u h u F u l u ] ! a st= u l u h u F a ! ] u i N
Mutation: Niu]!aFuhulu
Inversion(gst): u l u h u F a ! ] u i N (st):N i u ] ! F u l u h u a
=
N I u h u F
U L u ] ! a
gst= N i u h u F u l u ] ! a
Fit value: Comparing st and gst to find out
no. of characters whose position remained unchanged.
fit(st,gst)=4
=
N i u ] ! a
U l u h u F
N i u ] u F
u l u h ! a
gst= N i u ] u F u l u
h ! a
when i=1 ie, ODD
c: =c + ASCII(text [i] )- (m* key_arr[j])=ASCII(„N‟)-(1*7)=G
when i=2 ie, EVEN
c: = c+text[i]+ (m* key_arr[j])= N+(ASCII(„i‟)+(1*6))= Go
fit(st,gst)=3

__________________________________________________________________________________________
N i u ] ! F
u l u h u a
=
N i u ] ! a
u l u h u F
gst=
N i u ] ! F u l u h u a
fit(st,gst)=2
So fittest string(fst) having minimum fit value(2) = N
i u ] ! F u l u h u a
STEP 2: Decryption using Private Key: Input Private
Key(x,y)=(14,3)
Input intermediate cipher: Niu]!Fuluhua
Read Public key(p,q)=(464,453)
from Database
x: = ((x-(y-1))*y) +y
=((14-(3-1))*3)+3=39 p: = ((p-(q-1))*q) +q
=((464-(453-1))*453)+453=5889
(x/y=p/q=13) and (x*q=p*y=17667)
n: = x*q=39*453=17667 key_arr[]={7,6,6,7,1} m:=1 and c=””
Finally we get the intermediate
cipher : Good Morning
CONCLUSIONS
In this work, a heuristic for network security in using genetic
algorithm in computer networks has been proposed. Here an
asymmetric cryptographic approach is implemented to ensure
confidentiality in networks, which is again designed and
implemented with genetic algorithm.
ACKNOWLEDGEMENTS
The authors of this research would like to thank B. P. Poddar
Institute of Management and Technology for providing high-
end computing laboratories during their research work.
REFERENCES
[1] Kartalopoulos, S.V. “Differentiating Data Security and
Network Security”, IEEE Communications, 2008. ICC‟08.
International Conference on Telecommun. Networking, Univ.
of Oklahoma, Tulsa, OK, pp. 1469 – 1473, Issue Date: 19-23,
May 2008.
[2] Stamatios V. Kartalopoulos, Williams Professor in
Telecommunications Networking, “Optical Network Security:
Countermeasures in View of Channel Attacks”, Military
Communications Conference,
2006. MILCOM 2006. IEEE , The University of Oklahoma,
Washington, DC, ISBN: 1-4244-0617-X, pp. 1-5, Issue Date:
23-25 Oct, 2006.
[3] Vulnerabilities and Security strategy for the Next
Generation bandwidth Elastic Pon Stamatios
V.Kartalopous,Di Jin Ece department,TCOM Graduate
program , The University of Oklahoma , 4502 E.41 st
Street,Tulsa,OK 74135 USA.
[4] Cryptography and Network Security , by Forouzon.
[5] An Introduction to Neural Network ,Kevin Gurney.
BIOGRAPHIES
Soumya Paul, Assoc. Professor and Head, Department of
Computer Application in B. P. Poddar Institute of
Management & Technology, Kolkata, has been in teaching
and research for over 12 years. He holds a Master‟s Degree in
Technology, Computer Application as well as in Mathematics
and has gathered vast experiences in the same. He received his
M.Sc. (Mathematics) from Visva Bharati University and stood
1st class 1st. He received MCA from National Institute of
Technology, Rourkella and M. Tech (CSE) from AAI-Deemed
University and pursuing Ph. D in Computer Science and
Engineering. He served as a faculty member and visiting
faculty member in various Institutes and Universities like
RCCIIT, Visva Bharati University, University of Calcutta,
Bardhaman University, West Bengal University of
Technology etc. He has delivered numerous lectures across
India in the field of his research interest, Optical Networks and
Genetic Algorithms. He is an author/co-author of several
published articles in International Journals and International
Conferences. He has chaired an International Conference
technically supported by IEEE communication. He has more
than 15 research publications and currently Reviewer and
Member, Editorial Board in many conferences and journals
like International Journal of Data Modelling and Knowledge
Management.
Inadyuti Dutt has been in the field of academics for more
than ten years and currently the Assistant Professor of Dept. of
Computer Application, B.P.Poddar Institute of Management &
Technology, West Bengal, India. Earlier she held various
technical positions in National Informatics Centre, Kolkata ,
Semaphore Computing Network etc. She has earned Master‟s
degree in Computer Application and currently pursuing her
research in Computer Science & Engineering. She has more
than 20 publications to her laurels and her research interest is
specifically in the field of Optical Networking, Security and
Genetic Algorithms. She has also been Member, Editorial
Board in journal publications

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S.N. Chaudhuri, Director Kanad Institute of Engineering &
Management, Mankar, Burdwan, West Bengal is a renowned
Academician as well as a world famous Scientist. He has
working experience for nearly 40 years in different National
and International Institutions. As visiting Professor, he visited
different foreign Universities. He is having a number of
publications to his credit and his name has been included in
who is Who Indian Personages.

Design and implementation of network security using genetic algorithm

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Design and implementation of network security using genetic algorithm