ICTACT Journal on Soft Computing

Open Access Open Access  Restricted Access Subscription Access
Open Access Open Access Open Access  Restricted Access Restricted Access Subscription Access

Mutual Learning in Tree Parity Machines Using Cuckoo Search Algorithm for Secure Public Key Exchange


Affiliations
1 Department of Computer Science and Engineering, Netaji Subhash Institute of Technology, India
2 Department of Electronics and Communication Engineering, Netaji Subhash Institute of Technology, India
     

   Subscribe/Renew Journal


In Neural Cryptography, Artificial Neural Networks are used for the process of key generation and encryption. Tree Parity Machine (TPM) is a single layer neural network that approaches symmetric key exchange using the process of mutual learning. This method is exploited to design a secure key exchange protocol, where the sender and the receiver TPMs are synchronized to obtain an identically tuned weight vectors in both the networks. The synchronized TPMs are then capable of generating a key stream. The time required for synchronization depends on the initial weight vectors which are randomly initialized. In the proposed method, the process of synchronization is expedited using Cuckoo Search (CS) Algorithm used for the generation of optimal weights.

Keywords

Synchronisation, Tree Parity Machine, Cuckoo Search Algorithm, Key Exchange, Security.
Subscription Login to verify subscription
User
Notifications
Font Size

  • Whitfield Diffie and Martin Hellman, “New Directions in Cryptography”, IEEE Transactions on Information Theory, Vol. 22, No. 6, pp. 644-654, 1976.

  • Ido Kanter, Wolfgang Kinzel and Eran Kanter, “Secure Exchange of Information by Synchronization of Neural Networks”, Europhysics Letters, Vol. 57, No. 1, pp. 141-148, 2002.

  • Pravin Revankar, W.Z. Gandhare and Dilip Rathod, “Neural Synchronization with Queries”, Proceedings of International Conference on Signal Acquisition and Processing, pp. 233-239, 2010.

  • Xin-She Yang and Suash Deb, “Cuckoo Search via Levy flights”, Proceedings of International Conference on Nature and Biologically Inspired Computing, pp. 331-336, 2009.

  • Xin-She Yang and Suash Deb, “Engineering Optimization by Cuckoo Search”, International Journal of Mathematical Modelling and Numerical Optimization, Vol. 1, No. 4, pp. 330-343, 2010.

  • Natalia Caporale and Yang Dan, “Spike Timing-Dependent Plasticity: A Hebbian Learning Rule”, Annual Review of Neuroscience, Vol. 31, pp. 25-46, 2008.

  • Wolfgang Kinzel, “Theory of Interacting Neural Networks”, Proceedings of International Conference on Disordered Systems and Neural Networks, pp. 311-318, 2002.

  • Wolfgang Kinzel and Ido Kanter, “Neural Cryptography”, Proceedings of 9th International Conference on Neural Information Processing, Vol. 3, pp. 688-695, 2002.

  • Alexander Klimov, Anton Mityagin and Adi Shamir, “Analysis of Neural Cryptography”, Proceedings of International Conference on the Theory and Application of Cryptology and Information Security, pp. 23-29, 2002.

  • Lanir N. Shacham, et al., “Cooperating Attackers in Neural Cryptography”, Physical Review E, Vol. 69, No. 6, pp. 137-146, 2004.

  • Jiawei Yuan and Shucheng Yu, “Privacy Preserving Back-Propagation Neural Network Learning made Practical with Cloud Computing”, IEEE Transactions on Parallel and Distributed Systems, Vol. 25, No. 1, pp. 212-221, 2014.

  • Ning Cao et al., “Privacy-Preserving Multi-Keyword Ranked Search over Encrypted Cloud Data”, IEEE Transactions on Parallel and Distributed Systems, Vol. 25, No. 1, pp. 222-233, 2014.

  • R. Tso, X. Huang and W. Susilo, “Strongly Secure Certificate Less Short Signatures”, Journal of Systems and Software, Vol. 85, No. 6, pp. 1409-1417, 2012.

  • Ahmed M. Allam, Hazem M. Abbas and M. Watheq El-Kharashi, “Authenticated Key Exchange Protocol using Neural Cryptography with Secret Boundaries”, Proceedings of International Conference on Neural Networks, pp. 23-34, 2013.

  • Thuan Thanh Nguyen, Anh Viet Truong and Tuan Anh Phung, “A Novel Method based on Adaptive Cuckoo Search for Optimal Network Reconfiguration and Distributed Generation Allocation in Distribution Network”, International Journal of Electrical Power and Energy Systems, Vol. 78, pp. 801-815, 2016.

  • R. Rao, “Review of Applications of TLBO Algorithm and a Tutorial for Beginners to Solve the Unconstrained and Constrained Optimization Problems”, Decision Science Letters, Vol. 5, No. 1, pp. 1-30, 2016.


Abstract Views: 260

PDF Views: 2




  • Mutual Learning in Tree Parity Machines Using Cuckoo Search Algorithm for Secure Public Key Exchange

Abstract Views: 260  |  PDF Views: 2

Authors

Shikha Gupta
Department of Computer Science and Engineering, Netaji Subhash Institute of Technology, India
Nalin Nanda
Department of Electronics and Communication Engineering, Netaji Subhash Institute of Technology, India
Naman Chhikara
Department of Electronics and Communication Engineering, Netaji Subhash Institute of Technology, India
Nishi Gupta
Department of Computer Science and Engineering, Netaji Subhash Institute of Technology, India
Satbir Jain
Department of Computer Science and Engineering, Netaji Subhash Institute of Technology, India

Abstract


In Neural Cryptography, Artificial Neural Networks are used for the process of key generation and encryption. Tree Parity Machine (TPM) is a single layer neural network that approaches symmetric key exchange using the process of mutual learning. This method is exploited to design a secure key exchange protocol, where the sender and the receiver TPMs are synchronized to obtain an identically tuned weight vectors in both the networks. The synchronized TPMs are then capable of generating a key stream. The time required for synchronization depends on the initial weight vectors which are randomly initialized. In the proposed method, the process of synchronization is expedited using Cuckoo Search (CS) Algorithm used for the generation of optimal weights.

Keywords


Synchronisation, Tree Parity Machine, Cuckoo Search Algorithm, Key Exchange, Security.

References