Genetic Key Guided Neural Deep Learning based Encryption for Online Wireless Communication (GKNDLE)

Transcription

1 Genetc Key Guded Neural Deep Learnng based Encrypton for Onlne Wreless Communcaton (GKNDLE) Arndam Sarkar Department of Computer Scence & Electroncs, Ramakrshna Msson Vdyamandra, Belur Math , West Bengal, Inda. Abstract In ths paper, a neural deep learnng guded genetc secret key based technque for encrypton (GKNDLE) has been proposed for onlne transmsson of data/nformaton through wreless communcaton. In ths approach both the communcatng networks receve an ndstngushable nput vector, produce an output bt and are traned based on the output bt. Synchronzed weght vectors become the chromosome of the 1 st generaton Genetc Algorthm based secret key used to encrypt the plan text. Intermedate encrypted stream goes through another trangular cpher process. A secret key s also fabrcated n the process of encrypton as cascaded manner. Ths ntermedate cpher text s agan encrypted to form the fnal cpher text through channg and cascaded xorng of dentcal weght vector wth the dentcal length ntermedate cpher text block. If sze of the last block of ntermedate cpher text s less than the sze of the key then ths block s kept unchanged. Recever wll use dentcal weght vector for performng decpherng process for gettng the trangular encrypted cpher text and secret key for decodng. Fnally GA secret key helps to generate ultmate plan text. A sesson key based transmsson has also been proposed usng 161-bt key format of 14 dfferent segments [7,8]. Parametrc tests are done and results are compared n terms of Ch-Square test, response tme n transmsson wth some exstng classcal technques, whch shows comparable results for the proposed system. Keywords: Deep Learnng, weght vector, nput vector, sub key, sesson key, channg, wrappng technque. INTRODUCTION These days a varety of technques are avalable to defend data and nformaton from eavesdroppers [2-9, 11, 20, 21, 22]. Every algorthm has ts own advantages and dsadvantages. For Example n DES, AES algorthms [1] the cpher block length s nflexble. RBCMPCC [14] allow only one cpher block encodng. In RPSP algorthm [15] any ntermedate blocks durng ts cycle taken as the encrypted block and ths number of teratons acts as secret key. The organzaton of ths paper s as follows. Secton II of the paper deals wth the proposed synchronzaton and key generaton technque and also random block length based cryptographc technques. Sesson key based encrypton technque has been dscussed n secton III. Example of ths technque s gven n secton IV. Complexty of the algorthm s presented n secton V. Results are presented n secton VI. Analyss regardng varous aspects of the technque has been presented n secton VII. Conclusons are drawn n secton VIII and that of references at end. THE TECHNIQUE In proposed technque Genetc key s used to encrypt the plan text. Then ths ntermedate GA encrypted stream goes through another round of trangular encrypton process. Secret key s padded wth the last block of ntermedate trangular encrypted text. Ths ntermedate and padded cpher text s agan encrypted to form the fnal cpher text usng channg of cascaded xorng of ntermedate cpher text wth the dentcal neural secret key. Usng the same weght vector recever performs the reverse technque. Secton II.A deals wth key generaton technque and that of secton II.B and II.C descrbes the encrypton and decrypton technques respectvely. A. Neural synchronzaton scheme & secret key At the begnnng of the transmsson, a neural network based secret key generaton s performed between recever and sender. The same may be done through the prvate channel also or t may be done n some other tme, whch s absolutely free from encrypton. Fgure 1 shows the tree based generaton process usng random number of nodes (neurons) and the correspondng algorthm s gven n twosub secton II.A.1 and II.A.2. Fgure 1. A tree party machne wth K=3 and N=4 3631

2 .A.1. Neural Synchronzaton Algorthm Input: - Random weghts, nput vectors for both neural networks Output: - Secret key through synchronzaton of nput and output neurons as vectors. Method: - Each party (A and B) uses ts own (same) tree party machne. Neural network parameters: K, N, L s wll be dentcal for both partes and synchronzaton of the tree party machnes s acheved. Parameters: K - The number of hdden neurons. N - The number of nput neurons connected to each hdden neuron, total (K*N) nput neurons. L - The weght {-L..+L} Step 1. Intalze random weght s. Step 2. Repeat step 3 to 6 untl the full synchronzaton s acheved, usng hebban-learnng rules (eq. 3). Step 3. Generate random nput vector X. Inputs are generated by a thrd party (say the server) or one of the communcatng partes (fg. 2) Fgure 2. Neural network for sender and recever Step 4. Compute the s of the hdden neurons 1 f N sgn wj xj sgn x 0 f j1 1 f Step 5. Compute the of the output neuron K sgn N 1 j 1 x 0, x 0, x 0. w j x j Step 6. Compare the output s of both tree party machnes by exchangng between the networks Outputs are not same: Go to step 3 f Output (A) Output (B) else f Output (A) = Output (B) then one of the sutable learnng rule s appled to the weghts. Update the weghts only f the fnal output s of the neural machnes are (1) (2) equvalent. In ths paper we have used deep hebban-learnng rule for synchronzaton (eq. 3). When synchronzaton s fnally occurred, the synaptc weghts are same for both the networks. In each step there may be three possbltes: w w x A B 1. Output (A) Output (B): None of the partes updates ts weghts. 2. Output (A) = Output (B) = Output (E): All the three partes update weghts n ther tree party machnes. 3. Output (A) = Output (B) Output (E): Partes A and B update ther tree party machnes, but the attacker cannot do that. Because of ths crcumstance hs learnng s slower than the synchronzaton of partes A and B. The synchronzaton of two partes s faster than learnng of an attacker. Complexty: O(N) computatonal steps are needed to generate a key of length N. The average synchronzaton tme up to N=1000 asymptotcally one expects an ncrease lke O (log N). A.2. Fnal Key generaton usng Genetc Algorthm The technque consders the synchronzed weght vector from the n the form of blocks of bts wth dssmlar sze lke 8/16/32/64/128/256 becomes the 1 st chromosome of the 1 st generaton. Steps of Random numbers generaton (eq. 4) usng genetc functons,.e., crossover and mutaton respectvely. Assumpton about the generaton of random numbers: - Chromosomes representaton n Bnary, Populaton Sze s 10 (could be vared), Hence 5 generatons are requred to generate 50chromosomes f the number of block of characters s 50.The sequence of random numbers (chromosome) s generated va the followng teratve equaton. X ( a * X ) mod m (3) n1 nc (4) Where, Xn denotes the randomly selected 8 bts synchronzed weght vector whch becomes frst chromosome of the frst generaton, m -modulus (m > 0), a - multpler (0 <= a < m),c -ncrement (0 <= c < m). After cratng the 1 st generaton of the chromosomes, next generaton chromosomes are generated usng the GA operators CROSSOVER followed by MUTATION. In case of even generaton crossover s taken place from top to bottom n a sequental way by parng up chromosomes lke (1,2) (3,4) (5,6).In case of odd generaton crossover s take place from bottom to top n a sequental way by parng up chromosomes lke (50,49) (48,47) (46,45) Consder 284, 7000, 9024, 4025, 1235, 2564, 654, 6526, 3652, 124, as frst generaton chromosomes. 2 nd generaton chromosomes are obtaned by Parng up lke (284, 7000), (9024, 4025), (1235, 2564), (654, 6526), (3652, 124) 3632

3 Here, crossover and mutaton wll be perform let at 4 th locus of the gene of chromosome. Consder bnary representaton of chromosomes as 284= = The output stream on crossover s The output generated on mutaton s = =6428 (Mutaton of 4 th bt). Up to 5 th generaton same technque s use to generate 50 chromosomes (Each generaton 10 populatons) and get the fnal set of numbers. Encrypton 1. Once all the numbers are generated then let ths array of numbers be called GENETIC_ARRAY and select the LAST dgt of each number from GENETIC _ARRAY and a new collecton of numbers s generated and let ths collecton s called CODED_ARRAY. 2. Use ths numbers from CODED _ARRAY sequentally for substtutng on a one-to-one bass for the characters of the plan text. 3. Use ASCII s of the plan text characters and ADD the numbers of CODED_ARRAY from the ASCII s. For example the message ARINDAM the CIPHER TEXT wll be calculated accordng to followng method. LET GENETIC _ARRAY = {2365, 1211, 8526, 1258, 7639, 4584,3982} Table I: Genetc Algorthm Based Encrypton Character ASCII GENETIC_ARRAY Addton Result Value Number Taken sequentally A (F) R (S) I (O) N (V) D (M) A (E) M (O) ntermedate sub-stream S 1 s also then par-wse XORed to generate S 2 = s 2 0 s 2 1 s 2 2 s 2 3 s 2 4 s 2 5 s 2 n-3, whch s the 2 nd ntermedate sub-stream of length (n-2). The method terates (n-1) tmes to produce S n-1 = s n-1 0, whch s a sngle bt only. So, the sze of the 1 st mdway sub-stream s one bt fewer than the source sub-stream; the sze of each of the ntermedate sub-streams startng from the 2 nd one s one bt less than that of the sub- stream wherefrom t was generated; and fnally the sze of the fnal sub-stream n the process s one bt less than the fnal ntermedate sub-stream. Fgure 3 shows the process. Table II: Optons for choosng Target Block from Trangle Opton Target Block Seral No s 0 0 s 1 0 s 2 0 s 3 0 s 4 0 s 5 0 s n-2 0 s n-1 0 s n-1 0 s n-2 0 s n-3 0 s n-4 0 s n-5 0 s 1 0 s 0 0 s 0 n-1 s 1 n-2 s 2 n-3 s 3 n-4 s 4 n-5 s n-2 1 s n-1 0 s n-1 0 s n-2 1 s n-3 2 s n-4 3 s n-5 4 s 1 n-2 s 0 n-1 Method of Formaton Takng all the MSBs startng from the source block tll the last block generated Takng all the MSBs startng from the last block generated tll the source block Takng all the LSBs startng from the source block tll the last block generated Takng all the LSBs startng from the last block generated tll the source block The ntermedate cpher text s FSOVMEO from table I. Now ths ntermedate encrypted stream s consder as an nput source block to the next level of encrypton procedure. B.Ttrangularzaton Durng encrypton, consder a block S = s 0 0 s 0 1 s 0 2 s 0 3 s 0 4 s 0 5 s 0 n-2 s 0 n-1 of sze n bts, where s 0 = 0 or 1 for 0 <= <= (n- 1). Now, startng from MSB (s 0 0) and the next-to-msb (s 0 1), bts are par-wse XORed, so that the 1 st ntermedate substream S 1 = s 1 0 s 1 1 s 1 2 s 1 3 s 1 4 s 1 5 s 1 n-2 s generated consstng of (n-1) bts, where s 1 j = s 0 j s 0 j+1 for 0 <= j <= n- 2, stands for the exclusve OR operaton. Ths 1 st Opton Seral No. 010 Opton Seral No. 100 Opton Seral No. 001 Opton Seral No. 011 Fgure 3. Dagrammatc Representaton of Optons for choosng Target Block from Trangle Table II s for optons for choosng target block from trangle. Now sze of each encrypted block under consderaton and optons chosen for ths partcular block merged together. In ths way a ntermedate key s formed and padded wth the encrypted block. Then the neural secret key s use to xored wth the same length frst ntermedate cpher text block to produce the frst fnal cpher block (neural secret key XOR 3633

4 wth same length cpher text). Ths newly generated block agan xored wth the mmedate next block and so on. Ths channg of cascaded xorng mechansm s performed untl all the blocks get exhausted. If the last block sze of ntermedate cpher text s less than the requre xorng block sze (.e. weght vector sze) then ths block s kept unchanged C. Decrypton Technque Durng decrypton, the encrypted message s converted nto the correspondng stream of bts and then ths stream s decomposed nto a fnte set of blocks, each consstng of a fnte set of bts usng same rule of encrypton. It s to be noted that the recever wll take dentcal weght vectors and same neural key generaton algorthm s use to generate the neural key. Then cascaded xorng operaton s performed usng dentcal neural secret key wth the cpher text. The technque of performng xorng s same that was n encrypton process. Fnally from the outcomes ntermedate encrypted block (E) and key block s extracted and now key s use to decpher the E to get the ntermedate source stream. To ease the explanaton of decrypton technque, let us consder, e 0-1 = s -1 n- for 1 <= <= n, so that the encrypted block becomes E = e 0 0 e 0 1 e 0 2 e 0 3 e 0 4 e 0 n-2 e 0 n-1. Now, followng the same approach as mentoned n secton 3.2.1, a trangle s to be formed. After the formaton of the trangle, for the purpose of decrypton, the block e n-1 0 e n-2 0 e n-3 0 e n-4 0 e n-5 0 e 1 0 e 0 0,.e., the block constructed by takng all the MSBs of the blocks startng from the fnally generated sngle-bt block E n-1 to E, are to be taken together and t s to be consdered as the decrypted block. Fgure 4 show the trangle generated and hence the decrypted block obtaned. Here the ntermedate blocks are referred to as E 1, E 2,, E n-2 and the fnal block generated as E n-1. Opton Seral No. 010 Opton Seral No. 100 Opton Seral No. 001 Opton Seral No. 011 Fgure 4. Generaton of Source Block from Target Result (R) Table III: Genetc Algorthm Based Decrypton For loop I =0 to 255 Do (I R) & Compare Wth CODED _ARRAY & Choose I Character A R I N D A M SESSION KEY GENERATION To ensure secured encrypton of the proposed technque wth varyng sze of blocks, a 161 bt key format consstng of 14 dfferent segment has been proposed here [7,8]. The secton of rank the R, there can exst a utmost of N=218-R blocks, each of unque sze of S=218-R, R startng from 1 and movng tll 14.Full nput s dvded nto blocks of varous szes based on the sesson key. Fgure bt key format Segment wth R=1 formed wth the frst maxmum blocks, each of sze bts Segment wth R=2 formed wth the frst maxmum blocks, each of sze bts Segment wth R=3,4,5,----,14 formed wth the next maxmum 32768, 16384,8192,----,16 blocks, each of sze 32768, 16384,8192, ,16 bts respectvely. Wth such a structure, the key space becomes 147 bts long and a stream of the maxmum sze 2.6 GB can be encrypted usng the proposed technque. The key structure s represented n Fgure 5.Ths sesson key may be used durng each sesson as a random manner to enhance the securty of wreless communcaton. EXAMPLE Fnally, table III Genetc Algorthm based decrypton procedure s use to get orgnal plan text. In ths secton, we consder Genetc Algorthm based encrypted stream as a source stream. Secton IV.A shows how the GA based encrypted text s further encrypted and secton IV.B descrbes the process of decrypton. 3634

5 A The Process of Encrypton: Fgure 6. Formaton of Trangle for S = Fgure 6 shows the trangle formaton of 1 st 8 bts source block. Table IV: Dfferent Target Blocks for S = Source Block S Target Block Correspondng to Seral No. Target Block T 001 T 1 = T 2 = T 3 = T 4 = Table IV shows that dfferent target (decrypted) block for a partcular source block dependng on dfferent opton seral no (001,010,011,100). For dfferent 8/16/32/64/128 bt blocks dfferent opton seral no can be chosen. B The Process of Decrypton: For target blocks T 1 = and T 4 = , the same approach s to be followed to generate the correspondng source blocks. But blocks T 2 = and T 3 = requre dfferent technques. Fgure 7 show the generatons of source block from target blocks T Fgure7. Source Block S = from Target Block T 2 = After stuffng the key wth the above mdway cpher text and xorng wth the neural secret key, fnal encrypted cpher text s generated. At the destnaton pont, recever s secret neural key s use to xorng the cpher text to get back the key and ntermedate cpher text. Now, by that secret key, the recever gets the nformaton on dfferent block lengths.in ths way all the source blocks of bts are regenerated and combnng those blocks n the same sequence and performng GA based decrypton the source stream of bts are obtaned to get the source message or the plantext. RESULTS In ths secton the results of mplementaton of the proposed technque has been presented n terms of encrypton decrypton tme, Ch-Square test, source fle sze vs. encrypton tme along wth source fle sze vs. encrypted fle sze. The results are also compared wth exstng RSA technque. Source Sze (bytes) Encrypton & decrypton tme Table V: ENCRYPTION / DECRYPTION TIME VS. FILE SIZE Encrypton Tme (s) Decrypton Tme (s) Proposed RPSP Encrypted ANNGKE Sze (bytes) Source sze Proposed RPSP ANNGKE Table V shows encrypton and decrypton tme wth respect to the source and encrypted sze respectvely. It s also observed the alternaton of the sze on encrypton. In fgure 8 stream sze s represented along X axs and encrypton / decrypton tme s represented along Y-axs. Ths graph s not lnear, because of dfferent tme requrement for fndng approprate random number for dfferent block sze. The decrypton tme s approxmately lnear, snce there s no random number generaton durng decrypton Encrypton Decrypton Fgure 8. Source sze vs. encrypton tme & decrypton tme 3635

6 Table VI shows Ch-Square for dfferent source stream sze after applyng dfferent encrypton algorthms. It s seen that the Ch-Square of ANNGKE s better compared to the algorthm RBCMPCC and comparable to the Ch-Square of the RSA algorthm. Stream Sze (bytes) Table VI: Source sze vs. Ch-Square Ch- Square (TDES) Ch-Square (Proposed ANNGKE) Fgure 9 shows graphcal representaton of table VI. Fgure 9. Source sze vs. Ch-Square COMPLEXITY OF THE TECHNIQUE Ch-Square (RBCM CPCC) Ch- Square (RSA) The proposed technque has a complexty of O(L), whch can be computed usng followng three steps. Step 1:To generate a key of length N needs O(N) Computatonal steps. The average synchronzaton tme s almost ndependent of the sze N of the networks, at least up to N=1000.Asymptotcally one expects an ncrease lke O (log N). Step 2: Complexty of the encrypton technque s O(L). Step2.1:Genetc Algorthm based encrypton technque of the source block S = s 0 s 1 s 2 s 3 s 4 s L-1, takes O (L) amount of tme. Step 2.2:Trangular encrypton process takes O(L). Step 2.3: Neural weght encrypton technque s carred out whch generates the complexty as O (L). Step 3: Complexty of the decrypton technque s O(L). Step 3.1: Neural weght decrypton technque. So tme complexty s O(L). Step 3.2: Trangular decrypton process takes O(L). Step3.3:In Genetc Algorthm based decrypton technque, complexty to convert T nto the correspondng stream of bts S = s 0 s 1 s 2 s 3 s 4 s L-1, whch s the source block s O(L) as ths step also takes constant amount of tme for mergng s 0 s 1 s 2 s 3 s 4 s L-1. So, overall tme complexty of the entre technque s O(L). ANALYSIS OF RESULT From expermental results t s clear that the proposed technque may acheve optmal performances. Encrypton tme and decrypton tme vares almost lnearly wth respect to the block sze For the proposed algorthm Ch-Square s very hgh compared to some exstng algorthms. In the RBCMCPCC [14] a user nput key (mnmum length of eght byte) and a set of arbtrarly generated sesson keys are used to encrypt a source block. But ths key generaton technque s not sustanable aganst attack. Because RBCMCPCC key potency thoroughly depend upon strengthness of user nput key. A user nput key has to transmt over the publc channel all the way to the recever for performng the decrypton procedure. So there s a lkelhood of attack at the tme of key exchange. To defeat ths nsecure secret key generaton technque a neural network based secret key generaton technque has been proposed. The securty ssue of RBCMCPCC and RPSP [15] algorthm can be mproved by usng neural secret sub key generaton technque. In ths case, the two partners A and B do not have to share a common secret but use ther ndstngushable weghts as a secret key needed for encrypton. The fundamental concepton of neural network based key exchange protocol [10,12,13,16,17,18,19] focuses mostly on two key attrbutes of neural networks. Frstly, two nodes coupled over a publc channel wll synchronze even though each ndvdual network exhbts dsorganzed behavor. Secondly, an outsde network, even f dentcal to the two communcatng networks, wll fnd t exceptonally dffcult to synchronze wth those partes, those partes are communcatng over a publc network. An attacker E who knows all the partculars of the algorthm and records through ths channel fnds t thorny to synchronze wth the partes, and hence to calculate the common secret key. Synchronzaton by mutual learnng (A and B) s much qucker than learnng by lstenng (E) [17]. For usual cryptographc systems, we can mprove the safety of the protocol by ncreasng of the key length. In the case of neural cryptography, we mprove t by ncreasng the synaptc depth L of the neural networks. For a brute force attack usng K hdden neurons, K*N nput neurons and boundary of weghts L, gves (2L+1)KN possbltes. For example, the confguraton K = 3,L = 3 and N = 100 gves us 3*10253 key possbltes, makng the attack unfeasble wth today s computer power. E could start from all of the (2L+1)3N ntal weght vectors and calculate the ones whch are consstent wth the nput/output sequence. It has been shown, that all of these ntal states move towards the same fnal weght vector, the key s unque. Ths s not true for smple perceptron the most unbeaten cryptanalyss has two supplementary ngredents frst; a group of attacker s used. Second, E makes extra tranng steps when A and B are quet. So ncreasng synaptc depth L of the neural networks we can make our neural network safe. 3636

7 CONCLUSIONS Ths paper presents a novel approach for generaton of secret key proposed algorthm usng neural cryptography. Ths technque enhances the securty features of the RBCMCCC and RPSP algorthm by ncreasng of the synaptc depth L of the neural networks. In ths case, the two partners A and B do not have to exchange a common secret key over a publc channel but use ther ndstngushable weghts as a secret key needed for encrypton or decrypton. So lkelhood of attack proposed technque s much lesser than the smple RBCMCCC algorthm. REFERENCES [1] Kahate, A. (2010). Cryptography and Network Securty, 2 nd edton, Tata McGraw Hll. [2] Dffe, W., & Hellman, M. (1976). New drectons n cryptography, IEEE Trans. Inform. Theory, 22(6), pp [3] Zhang, R., Shen, J., We, F., L, X., & Sangaah, A. K. (2017). Medcal mage classfcaton based on multscale non-negatve sparse codng. Artfcal Intellgence n Medcne. [4] Anurag Roy and Asoke Nath, DNA Encrypton Algorthms: Scope and Challenges n Symmetrc Key Cryptography, IJIRAE [5] Kals, S., Kaur, H. & Chang, V. J Med Syst (2018) 42: Sprnger US, ISSN: [6] Lao, X., Yn, J., Guo, S., L, X., & Sangaah, A. K. (2017). Medcal JPEG mage steganography based on preservng nterblock dependences. Computers & Electrcal Engneerng. [7] Anusudha, K., Venkateswaran, N. & Valarmath, J. Multmed Tools Appl (2017) 76 (2), pp , Sprnger US, Prnt ISSN , Onlne ISSN [8] Al-Haj, A., Mohammad, A. & Amer(2017) A. J Dgt Imagng 30(1), pp 26 38https://do.org/ /s [9] Chen, C., Xang, H., Qu, T., Wang, C., Zhou, Yang., Chang, V. A rear-end collson predcton scheme based on deep learnng n the Internet of Vehcles, Journal of Parallel and Dstrbuted Computng, [10] Sarkar, A., & Mandal, J. K. (2014). Cryptanalyss of Key Exchange method n Wreless Communcaton (CKE). Internatonal Journal of Network Securty (IJNS), 17(4), , ISSN [Onlne]; X [Prnt]. [11] Sarkar, A., & Mandal, J. K. (2014). Computatonal Scence guded Soft Computng based Cryptographc Technque usng Ant Colony Intellgence for Wreless Communcaton (ACICT). Internatonal Journal of Computatonal Scence and Applcatons (IJCSA), 4(5), 61-73, DOI: /jcsa , ISSN [12] Sarkar, A., & Mandal, J. K. (2014). Soft Computng based Cryptographc Technque usng Kohonen's Self- Organzng Map Synchronzaton for Wreless communcaton (KSOMSCT). Internatonal Journal n Foundatons of Computer Scence & Technology (IJFCST), 4(5), , DOI: /jfcst , ISSN [13] Sarkar, A., & Mandal, J. K. (2013). Computatonal Intellgence based Trple Layer Perceptron Model Coordnated PSO guded Metamorphosed based Applcaton n Cryptographc Technque for Secured Communcaton (TLPPSO). In Proceedngs of the Frst Internatonal Conference on Computatonal Intellgence: Modelng, Technques and Applcatons (CIMTA-2013), Vol.10, pp , DOI: /j.protcy , ISSN: , September , Department of Computer Scence & Engneerng, Unversty of Kalyan, Kalyan, Inda, Proceda Technology, Elsever. [14] Jayanta Kumar Pal, J. K. Mandal A Random Block Length Based Cryptosystem through Multple Cascaded Permutaton Combnatons and Channg of Blocks Fourth Internatonal Conference on Industral and Informaton Systems, ICIIS 2009, 28 31December2009, SrLanka. [15] J. K. Mandal, et al, A 256-bt Recursve Par Party Encoder for Encrypton, Advances n Modelng, D; Computer Scence & Statstcs (AMSE), vol. 9, No. 1, pp. 1-14, France, [16] T. Godhavar, N. R. Alanelu and R. Soundararajan Cryptography Usng Neural Network IEEE Indcon 2005 Conference, Chenna, Inda, Dec ggggjgggggggggggg [17] Wolfgang Knzel and ldo Kanter, "Interactng neural networks and cryptography", Advances n Sold State Physcs, Ed. by B. Kramer (Sprnger, Berln. 2002), Vol. 42, p. 383 arxv- cond-mat/ [18] Wolfgang Knzel and ldo Kanter, "Neural cryptography" proceedngs of the 9 th nternatonal conference on Neural Informaton processng(iconip 02).h [19] Dong Hu "A new servce based computng securty model wth neural cryptography"ieee07/2009.j [20] Jha P. K., Mandal, J. K., Encrypton Through Cascaded Recursve Key Rotaton of a Sesson Key wthtransposton and Addton of Blocks (CRKRTAB) Proceedngs of Natonal conference on Recent Trends n Informaton Systems 2006, organzed by IEEE Kolkata secton, Jadavpur Unversty, Kolkata held on July 14-15th, pp [21] Jha P. K., Nayak, A.K., An Encrypton Technque through Btwse Operaton of Blocks (BOB) accepted tothe 97th Indan Scence Congress, to be held n Kerala, Jan 2010, Inda. [22] Saurabh Dutta, Ph. D. Thess An Approach Towards Development of Effcent encrypton Technques The Unversty of North Bengal, Inda,