Chaos-based Encryption Keys and Neural Key-store for Cloudhosted Data Confidentiality

Abstract

Cloud computing brings flexible and cost effective services. However, security concerns plague the cloud. Data confidentiality is one of the concerns inhibiting the adoption of cloud computing. This concern stems from various cyberattacks directed towards gaining unauthorised access to cloud-bound or cloud-hosted data. This paper proposes a client-end encryption and key management system to curb attacks that targets compromising the confidentiality of cloud-hosted data. The proposed system uses chaotic atmospheric noise to generate a fitness function. The fitness function generates random numbers which create encryption keys. The strength of the encryption keys is derived from the chaotic and random nature of the atmospheric noise. The keys are then used for encrypting cloud-bound data using Advanced Encryption Standard (AES-128, 192 and 256), Data Encryption Standard (DES), 3-DES, and our novel cryptosystem named Cryptor, before it can be sent to the cloud. However, encryption bears no significance if the key management is flawed. To address the inherent key management problem, the solution uses a neural network to learn patterns of an encryption key. Once learnt, the key is then discard to thwart possible key attacks. The key is reconstructed by the neural network for decryption purposes.