Cryptography Using Neural Network

Godhavari, T.; Alamelu, N.R.; Soundararajan, R.

doi:10.1109/indcon.2005.1590168

Cited by 33 publications

(16 citation statements)

References 3 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…If the synaptic depth (L) is increased, the complexity of a successful attack grows exponentially, but there is only a polynomial increase of the effort needed to generate a key. TPM based synchronization steps are as follows [3,4,5,6,7].…”

Section: Tree Parity Machine (Tpm)mentioning

confidence: 99%

“…• DHLP offers two hidden layers instead of one single hidden layer in TPM [3,4,5,6,7] • Instead of increasing number of hidden neurons in a single hidden layer DHLP introduces an additional layer (second hidden layer) which actually increased the architectural complexity of the network that in turn helps to make the attacker's life difficult to guessing the internal representation of DHLP.…”

Section: Double Hidden Layer Perceptron (Dhlp)mentioning

confidence: 99%

“…In TPM and DHLP based session key generation procedure, both sender and receiver use identical architecture. Both of these DHLP [2] and TPM's [3,4,5,6,7] start with random weights and identical input vector. The organization of this paper is as follows.…”

mentioning

confidence: 99%

See 2 more Smart Citations

Comparative Analysis of Tree Parity Machine and Double Hidden Layer Perceptron Based Session Key Exchange in Wireless Communication

Sarkar

Mandal

2015

Advances in Intelligent Systems and Computing

View full text Add to dashboard Cite

Abstract. In this paper, a detail analysis of Tree Parity Machine (TPM) and Double Hidden Layer Perceptron (DHLP) based session key exchange tecchnique has been presented in terms of synchronization time, space complexity, variability of learning rules, gantt chart, total number of threads and security. TPM uses single hidden layer in their architecture and participated in mutual learning for producing the tuned weights as a session key. DHLP uses two hidden layers instead of single. Addition of this extra layer enhances the security of the key exchange protocol. Comparisons of results of both techniques has been presented along with detail analysis.Keywords: Tree Parity Machine (TPM), Double Hidden Layer Perceptron (DHLP), session key, wireless communication. IntroductionThese days a range of techniques are available to exchange session key. Each technique has its own advantages and disadvantages. In key exchange the main security intimidation is Man-In-The-Middle (MITM) attack at the time of exchange the secret session key over public channel. Diffie-Hellman key exchange algorithm suffers from this MITM attack. Most of the key generation algorithms in Public-Key cryptography suffer from MITM attack [1]. Where intruders can reside middle of sender and receiver and tries to capture all the information transmitting from both parties. Another noticeable problem is that most of the key generation algorithms need large amount of memory space for storing the key but now-a-days most of the handheld wireless devices have a criterion of memory constraints. In proposed DHLPSKG, problem MITM attack of Diffie-Hellman Key exchange [1] has been set on. In TPM and DHLP based session key generation procedure, both sender and receiver use identical architecture. Both of these DHLP [2] and TPM's [3,4,5,6,7] start with random weights and identical input vector. The organization of this paper is as follows. Section 2 of the paper deals with the TPM synchronization algorithm. DHLP based protocol for generation of session key

show abstract

Section: Tree Parity Machine (Tpm)mentioning

confidence: 99%

Section: Double Hidden Layer Perceptron (Dhlp)mentioning

confidence: 99%

See 1 more Smart Citation

Comparative Analysis of Tree Parity Machine and Double Hidden Layer Perceptron Based Session Key Exchange in Wireless Communication

Sarkar

Mandal

2015

Advances in Intelligent Systems and Computing

View full text Add to dashboard Cite

show abstract

“…In order to construct an authentication certificate, GSMLPSA assumes that the group obtains a secret password which can be used to authenticate the exchange protocol. This password can be mapped to multilayer perceptron guided cryptographic public parameter which can be used as an initial seed for a random number generator which encrypts the output bits in a fashion similar to that was proposed in [12,13]. Assume a random number generator (RNG), …”

Section: Gsmlpsa Certificate Generationmentioning

confidence: 99%

Group Session Key Exchange Multilayer Perceptron Based Simulated Annealing Guided Automata and Comparison Based Metamorphosed Encryption in Wireless Communication (GSMLPSA)

Sarkar¹,

Mandal²

2013

IJWMN

View full text Add to dashboard Cite

show abstract

“…In this case, the two partners A and B do not have to share a common secret but use their indistinguishable weights as a secret key needed for encryption. The fundamental conception of neural network based key exchange protocol [3,4,5,6] focuses mostly on two key attributes of neural networks. Firstly, two nodes coupled over a public channel will synchronize even though each individual network exhibits disorganized behavior.…”

Section: Encryption and Decryption Timementioning

confidence: 99%

An adaptive neural network guided secret key based encryption through recursive positional modulo-2 substitution for online wireless communication (ANNRPMS)

Mandal

Sarkar

2011

2011 International Conference on Recent Trends in Information Technology (ICRTIT)

View full text Add to dashboard Cite

In this paper, a neural network guided secret key based technique for encryption (ANNRPMS) has been proposed using recursive positional modulo-2 substitution for online transmission of data/information through wireless communication. In this approach both the communicating networks receive an indistinguishable input vector, produce an output bit and are trained based on the output bit. The dynamics of the two networks and their weight vectors are found to a novel experience, where the demonstrate networks synchronize to an identical time dependent weight vectors. This observable fact has been used to form a secured variable length secret-key using a public channel. The length of the key depends on the number of input and output neurons. The original plain text is encrypted using recursive positional modulo-2 substitution technique. The secret key also fabricated through encryption process in a cascaded manner. This intermediate cipher text is again encrypted to form the final cipher text through chaining and cascaded xoring of identical weight vector with the identical length intermediate cipher text block. If size of the last block of intermediate cipher text is less than the size of the key then this block is kept unchanged. Receiver will use identical weight vector for performing deciphering process for getting the recursive positional modulo-2 substitution encrypted cipher text and secret key for decoding. A session key based transmission has also been proposed using 161-bit key format of 14 different segments [7,8]. Parametric tests are done and results are compared in terms of Chi-Square test, response time in transmission with some existing classical techniques, which shows comparable results for the proposed system.

show abstract

Cryptography Using Neural Network

Cited by 33 publications

References 3 publications

Comparative Analysis of Tree Parity Machine and Double Hidden Layer Perceptron Based Session Key Exchange in Wireless Communication

Comparative Analysis of Tree Parity Machine and Double Hidden Layer Perceptron Based Session Key Exchange in Wireless Communication

Group Session Key Exchange Multilayer Perceptron Based Simulated Annealing Guided Automata and Comparison Based Metamorphosed Encryption in Wireless Communication (GSMLPSA)

An adaptive neural network guided secret key based encryption through recursive positional modulo-2 substitution for online wireless communication (ANNRPMS)

Contact Info

Product

Resources

About