Encryption technique based on chaotic neural network space shift and color-theory-induced distortion

Abstract

AbstractProtecting information privacy is likely to promote trust in the digital world and increase its use. This trust may go a long way toward motivating a wider use of networks and the internet, making the vision of the semantic web and Internet of Things a reality. Many encryption techniques that purport to protect information against known attacks are available. However, since the security challenges are ever-growing, devising effective techniques that counter the emerging challenges seems a rational response to these challenges. This paper proffers an encryption technique with a unique computational model that inspires ideas from color theory and chaotic systems. This mix offers a novel computation model with effective operations that (1) highly confuse plaintext and (2) generate key-based enormously complicated codes to hide the resulting ciphertext. Experiments with the prototype implementation showed that the proposed technique is effective (passed rigorous NIST/ENT security tests) and fast.