Network Security Implementation Using Genetic Algorithm in WDM Optical Networks

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Network Security Implementation Using Genetic

Algorithm in WDM Optical Networks

Soumya Paul, Inadyuti Dutt, Prof. (Dr.) S.N. Chaudhuri

Abstract - In this article, a genetic evolutionary algorithm is proposed to ensure the confidentiality in a transparent all optical networks. As the networks transport a huge traffic therefore the issues of information privacy become very important allow with security. Here an asymmetric cryptographic approach is implemented to ensure confidentiality in all optical networks.

Keywords - WDM, Cryptography, Encryption, Decryption, Cipher Text, Genetic Algorithm (GA).

I. I

NTRODUCTION

A. Wavelength Division Multiplexing (WDM) Optical

Networks

Optical networks are high capacity telecommunication network based on optical technologies and components that provide routing, grooming, and restoration at wavelength level as well as wavelength-based services. As networks face increasing bandwidth demand and diminishing fiber availability, network provides are moving towards a crucial milestone in network evolution: the optical networks. In an all-optical network (AON), all network-to-network interfaces are based on optical transmission. Optical networks often employ wavelength Division Multiplexing (WDM) to increase transmission capacity. WDM optical networks [1, 2] utilize light paths to exchange information between source-destination nodes-pairs. In WDM networks, a number of optical channels are carried in each fiber at disparate wavelengths. Normally it is required that the same wavelength be allocated on all the fiber links in the light path. This is known as the‘wave length continuity constraint’.

One of the serious problems with network transparency is that the properties of transparent optical components make AONs particularly vulnerable to various forms of attacks. In this paper an asymmetric cryptographic approach is implemented with genetic algorithm to ensure the confidentiality issue in a transparent all-optical network. Outline of remaining sections as follows: section II states the security of the files that contain confidential information and data in order to provide confidentiality, authentication, integrity, non-repudiation of the messages. The detail description of the proposed encryption-decryption algorithms are explained in section III. Finally the paper concludes in section IV.

II. P

ROBLEM

S

TATEMENT

This work mainly concerns about the security of the files that contain confidential information and data in order to provide confidentiality, authentication, integrity, non-repudiation of the messages using Genetic algorithm. An

asymmetric private key encryption algorithm is implemented to achieve the aforesaid purpose. The proposed heuristic takes any file as input from the user and produces an output file which actually contain some alpha-numeric data that cannot be understood by anyone. When the encrypted file is subjected to this application program, it is decrypted to retrieve the original file at the destination end. In asymmetric-key cryptography the plain text in encoded by managing it with a public key of user and decrypted by private-key of user2

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III. P

ROPOSED

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LGORITHM

A. Proposed Encryption Heuristic

Input: A plain text is taken as input file.

Output: Plain text is converted to a cipher text using a secret key.

Step1: Read the input file. Step2: Set a variable X,

where X=Position of alphabet according to order in a table.

Step3: The function is defined as f(X) = X2+1, 1≤X<10.

= X, X≥10.

= [X/3], X≥10 and even. G(i) = 2*i, i= row number.

Step4: A block of character of length n is taken from the input string.

Step5: Form an n×n matrix, where value of f(X) corresponding to the letter of the blocks in the column (ci) and G(i) where i is the corresponding row number(ri). Step6: The values are put into the matrix in this manner

(i, j)= ri+ cj, where i=1,2,…,n; j=1,2,…,n

where i denotes the row number and j denotes the column number.

Step7: From the table minimum value of the table is found out.

Step8: Allocate that value and omit the corresponding row and column.

Step9: Repeat Step7 and Step8 until the number of allocated cell is equal to the order of the matrix.

Step10: Allocated values are collected and its corresponding column value i.e f(X) is encrypted by its corresponding column i.e G(i)

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Step13: d=ri+ d.

Step 14: riis encrypted by the character of corresponding ASCII value d

Step 15: if I less than or equal to n then repeat the steps 13 and 14 else goto step 16.

Step 16: print the encrypted data. Step17: Stop.

B. Proposed Encryption Heuristic Using Genetic

Algorithm

Input: Encrypted text using 3.1algorithm.

Output: Proposed encryption Heuristic Using Genetic Algorithm into a cipher text.

Step1: Initialize Population size=5, Maximum number of generation=100, Crossover

Probability (Pcross)=0.99,

Mutation Probability (pmut)=0.99.

Step2: Input Strings are placed into a square matrix whose size is immediate square value of the string length.

Step3: A fitness function fi is defined as fi=maximum number of common term in a row i and find∑fi

Step4: Apply crossover on the matrix with mate number and X-site. Where X-site= the position of string where crossover has occurred.

Mate number (i,j)= crossover done between ith and jth row at X-site position.

Step5: Find the fitness value and compare with the previous result, if better result obtain then go to Step4 else go to Step6 The final matrix is stored in temp_pop. Step6: Apply mutation on the result where 1st bit of each string of temp_pop is exchanged with node position nk. The final string is stored in matrix final_pop.

Step7: Stop.

C. Proposed Decryption Heuristic Using Genetic

Algorithm

Input: Cipher text stored in final_pop matrix. Output: Intermediate text.

Step1: Apply mutation on the result, where 1stbit of each string of final_pop is exchanged with node position nkand the text is stored in temp_pop matrix.

Step2: Apply crossover on the temp_pop matrix given mate number and corresponding X site.

X-site= the position of string where crossover has occurred.

Mate number (i,j)= crossover done between ith and jth row at X-site position

Step3: Compare the result with the final_pop. If the result matrix matches with final_pop matrix then go to step4 else go to 2.

Step4: Print the encrypted data. Step5: d=65

Step6: ri= ASCII value of corresponding character where i=0,1,2,3

r0< r1< r2< r3 Step7: E= ri–(d+2*i)

Step8: character of corresponding ridecrypted by the value E.

Step9: print the encrypted data. Step10: Stop

D. Proposed Decryption Heuristic

Input : A cipher text and value of allocated cell(according to order) is taken as input.

Output: Cipher text is converted to a plain text using a Secret key.

Step1: Read the Input file.

Step2: Set Y=(value of allocated cell) - (value of corresponding row).

and T(Y) = Y, 26>Y>10 and odd number. = 3*Y+1, 3≤Y<10 and odd number = 3*Y and 3*Y+2, 3<Y<10 and even = Y1, Y-1 is perfect squar.

Step3: From input file taken the value of allocated cell and cipher text. Since each and every allocated cell has unique value, we apply the permutation the permutation on the set S. where S is a set collection of allocated values.

Step4: Then from Step2, get the value of T(Y). Step5: Apply Step2 to get the value of T(Y).

Step6: From the table get the letter corresponding to the value of T(Y).

Step7: Rearrange them according to order and get the plain text.

Step8: Print the plain text. Step9: Stop.

E. Proposed Cryptography Algorithm

Step1: A plain text and a is key is taken as input file. Step2: Apply proposed encryption heuristic procedure// subsection 5.2.1

Step3: Apply encryption heuristic using genetic algorithm on step2 // subsection 5.2.2

Step4: Encrypted data is to be send from sender to specify receiver.

Step5: Apply proposed decryption heuristic using genetic algorithm // subsection 5.2.3

Step6: Apply decryption heuristic on step5 // Subsection 5.2.4.

Step7: Stop.

F. Illustration of the proposed algorithm with

example

Input String: PLAIN TEXT- THIS IS MY PROJECT

G. Example Illustrating Encryption Heuristic

THIS

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@->0f(0)=1 I->9f(9)=82 S->19f(19)=19 @->0f(0)=1 Cipher text-2864 MY@P

M>13f(13)=13 Y->25f(25)=25 @->0f(0)=1 P->16f(16)=5 Cipher text-6824 ROJE

R->18f(18)=6 O->15f(15)=15 J->10f(10)=3 E->5f(5)=26 Cipher text-4628 CT@@

C->3f(3)=10 T->20f(20)=6 @->0f(0)=1 @->0f(0)=1 Cipher text-8624 Complete cipher text is--26842864682446288624

22+65=67C 44+67=71G 66+71=77M 88+77=85U Final cipher text

is---CMUGCUMGMUCGGMCUUMCG

H. Example illustrating encryption heuristic using

Genetic Algorithm

Final Cipher Text

is:-@MC@UMUUGCMGGCCGUUM@@@MCG

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CMUGCUMGMUCGGMCUUMCG

C67-65=2 G71-67=4 M77-71=6 U85-77=8. Decrypted Value 26842864682446288624

D. Example illustrating Decryption Heuristic

288-2=6 67171-6=65 89090-8=82 42323-4=19 618/20R/T 658H

829I 1919S

POSSIBLE PLAIN TEXT=R/THIS

61919-6=13 83333-8=25 233-2=1 499-4=5 1313M 2525Y

10@ 516P POSSIBLE PLAIN TEXT=MY@P

41010-4=6 62121-6=15 255-2=3 83434-8=2 618/20R/T 1515O 310J 265E POSSIBLE PLAIN TEXT=R/TOJE

81818-8=10 61212-6=6 233-2=1 455-4=1 103C 618/20R/T 10@ 10@ POSSIBLE PLAIN TEXT=CR/T@@ PLAIN TEXT IS:

R/THIS@IS@ MY@P R/TOJE CR/T@@

IV. S

IMULATION

R

ESULT AND

D

ISCUSSIONS

Input string:THIS IS MY PROJECT Result after algorithm 3.1:

CMUGCUMGMUCGGMCUUMCG Result after algorithm 3.2:

@MC@UMUUGCMGGCCGUUM@@@MCG

Result after algorithm 3.3:

CMUGCUMGMUCGGMCUUMCG Result after algorithm 3.4:

R/THIS@IS@ MY@P R/TOJE CR/T@@

V. C

ONCLUSION

In this work, a heuristic for network security in wavelength division multiplexing optical networks has been proposed. Here an asymmetric cryptographic approach is implemented to ensure confidentiality in All-Optical Networks which is again designed and implemented with genetic algorithm with the help of simulation result.

R

EFERENCES

[1] Mukherjee, B.: Optical Communication Networks. McGraw-Hill, NewYork(1997).

[2] Ramaswami,R, Sivarajan,K.N.:Optical Networls:A practical Perpective. Morgan Kaufmann Publish(1998).

[3] Distributed Algorithms for Attack Localization in All-Optical Networks Ruth Bergmann,Muriel M’edard, Serena Chann. Massachusetts Institute of Technology, Lincoin Laboratory. [4] Vulnerabilities and Security Strategy for the Next Generation

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Networks, Devices, and Technology

[6] “Optical Channel Signature in Secure Optical Networks” S.V. Kartalopoulos WSEAS Transactions on Communications [7] A New Approach to Optical Networks Security:Attack-Aware

Routing and Wavelength Assignment Nina Skorin-Kapov Member, IEE Jiajia Chen, Lena Wosinska, Member, IEEE.

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ROFILE

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 and research for more than ten years and is currently the Assistant Professor in the Department of Computer Application of B. P. Poddar Institute of Management & Technology, Kolkata, West Bengal, India. .Earlier, she held several technical positions in National Informatics Centre, Kolkata and Semaphore Computing Networks Pvt. Ltd. respectively. She has earned Master’s Degree in Computer Application and currently pursuing her research in Computer Science and Engineering. She has more than 15 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 like International Journal of Software Engineering & Research.