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Optimal Adaptive Genetic Algorithm Based Hybrid Signcryption Algorithm for Information Security

R. Sujatha1, M. Ramakrishnan2, N. Duraipandian3, B. Ramakrishnan4
Assistant Professor, Velammal Engineering College, Anna University, India.
Professor & Head, School of Computer Applications, Madurai Kamarajar University, India.
Principal, Velammal Engineering College, Anna University, India.
Associate Professor, Department of Computer Science and Research Centre, S.T. Hindu College, Madurai Kamarajar University, India.

Computer Modeling in Engineering & Sciences 2015, 105(1), 47-68. https://doi.org/10.3970/cmes.2015.105.047

Abstract

The functions of digital signature and public key encryption are simultaneously fulfilled by signcryption, which is a cryptographic primitive. To securely communicate very large messages, the cryptographic primitive called signcryption efficiently implements the same and while most of the public key based systems are suitable for small messages, hybrid encryption (KEM-DEM) provides a competent and practical way. In this paper, we develop a hybrid signcryption technique. The hybrid signcryption is based on the KEM and DEM technique. The KEM algorithm utilizes the KDF technique to encapsulate the symmetric key. The DEM algorithm utilizes the Adaptive Genetic Algorithm based Elliptic curve cryptography algorithm to encrypt the original message. Here, for the security purpose, we introduce the three games and we proved the attackers fail to find the security attributes of our proposed signcryption algorithm. The proposed algorithm is analyzed with Daniel of Service (DOS), Brute Force attack and Man In Middle (MIM) attacks to ensure the secure data transaction.

Keywords

Hybrid Signcryption, KEM, DEM, Adaptive Genetic Algorithm, Elliptic Curve Cryptography

Cite This Article

Sujatha, R., Ramakrishnan, M., Duraipandian, N., Ramakrishnan, B. (2015). Optimal Adaptive Genetic Algorithm Based Hybrid Signcryption Algorithm for Information Security. CMES-Computer Modeling in Engineering & Sciences, 105(1), 47–68.



This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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