SECURE COMMUNICATION WITH CHAOTIC DNA ENCRYPTION AND BIG DATA ANALYTICS

Abstract

The exponential growth of digital data transmission and storage demands robust encryption mechanisms capable of securing sensitive multimedia content across diverse platforms. This comprehensive review examines the convergence of chaotic systems, DNA encoding techniques, and big data analytics in developing next-generation image encryption algorithms. Through systematic analysis of recent advances in chaos-based cryptographic methods, this paper evaluates the effectiveness of hybrid approaches combining chaotic maps, DNA computing, and machine learning techniques for multimedia security. The study synthesizes findings from 24 peer-reviewed publications spanning 2022-2024, highlighting breakthrough methodologies in bit-level encryption, visual cryptography, and real-time security applications. Our analysis reveals that DNA-chaos hybrid systems achieve superior entropy rates (>7.99), correlation coefficients approaching zero, and processing speeds suitable for real-time applications. The integration of big data analytics enhances key generation mechanisms and provides adaptive security frameworks capable of responding to evolving cyber threats. These findings contribute significantly to the development of quantum-resistant encryption protocols and establish foundational principles for secure communication in the era of big data.