DeepOpt: a deep learning optimized privacy preservation framework for cyber-physical systems

Abstract

Privacy preservation and security enhancement are the key components of any network architecture due to advanced attack procedures. Cyber-Physical Systems (CPS) also need a mitigation and prevention strategy to deal with cyber threats. The existing approaches majorly deal with attack detection and focus on one or two attacks at a time. With this focus and demand of the CPS, this work proposes a deep learning optimized privacy preservation framework called DeepOpt. This proposed framework prevents the network from attackers and maintains security by classifying multiple attackers simultaneously using deep learning architecture. The proposed framework initializes privacy preservation using the trust-based approach and a hybrid optimization algorithm. In this, the network is divided into different zones, and each zone is secured using trust parameters with additional verification by secure hash function. The hybrid optimization selects the communication path using trust and energy that returns the attack-free path. This proposed architecture is simulated over different network scenarios with or without attacker nodes, and their traces are labeled to train the proposed deep convolutional neural network architecture. Finally, these models are integrated, and their performance is analyzed in different network scenarios and the presence of five different attackers such as blackhole, wormhole, man-in-the-middle attack, spoofing, and distributed denial of service. The simulation results, with improvement in detection accuracy, packet delivery ratio, and other performance factors, indicate the effectiveness of the proposed framework for both prevention and mitigation. Hence, this overall architecture preserves the privacy of CPS even in multifarious dynamic network scenarios.