AI Article Synopsis
- This paper introduces a novel method to boost the security and performance of the Serpent encryption algorithm by using Lorenz 96 chaos for sub-key generation and employing ECB parallel mode for encryption.
- The proposed technique has been implemented in Java and tested with RGB images using Python, showing significant resistance to various attacks while enhancing encryption quality metrics such as Shannon entropy and correlation coefficients.
- The method also reduces encryption/decryption time by over 61% and passes all tests in the Statistical Test Suite recommended by NIST, highlighting its effectiveness compared to other encryption schemes.
Article Abstract
This paper presents a new approach to enhance the security and performance of the Serpent algorithm. The main concepts of this approach is to generate a sub key for each block using Lorenz 96 chaos and then run the process of encryption and decryption in ECB parallel mode. The proposed method has been implemented in Java, openjdk version "11.0.11"; and for the analysis of the tested RGB images, Python 3.6 was used. Comprehensive experiments on widely used metrics demonstrate the effectiveness of the proposed method against differential attacks, brute force attacks and statistical attacks, while achieving superb results compared to related schemes. Moreover, the encryption quality, Shannon entropy, correlation coefficients, histogram analysis and differential analysis all accomplished affirmative results. Furthermore, the reduction in encryption/decryption time was over 61%. Moreover, the proposed method cipher was tested using the Statistical Test Suite (STS) recommended by the NIST and passed them all ensuring the randomness of the cipher output. Thus, the approach demonstrated the potential of the improved Serpent-ECB algorithm with Lorenz 96 chaos-based block key generation (BKG) and gave favorable results. Specifically, compared to existing encryption schemes, it proclaimed its effectiveness.
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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8725658 | PMC |
| http://dx.doi.org/10.7717/peerj-cs.812 | DOI Listing |
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