Deep Learning Based Distributed Intrusion Detection in Secure Cyber Physical Systems.

Cyber Physical Systems (CPSs) are network systems containing cyber (computation, communication) and physical (sensors, actuators) components that interact with each other through feedback loop with the help of human intervention. The dynamic and disseminated characteristics of CPS environment makes it vulnerable to threats that exist in virtualization process. Due to this, several security issues are presented in CPS. In order to address the challenges, there is a need exists to extend the conventional security solutions such as Intrusion Detection Systems (IDS) to handle high speed network data traffic and adaptive network pattern in cloud. Additionally, the identification of feasible network traffic characteristics is the main issue in precise detection of attacks in the network. With this motivation, the current research paper presents an Optimal Deep Belief Networkbased distributed Intrusion Detection System (ODBN-IDS) for secure CPS environment. The proposed model pre-process the cloud network traffic data to improve its quality to next level. Here, a Binary Flower Pollination Algorithm (BFPA) is employed for feature selection process. The attained characteristics are used in optimal Deep Belief Networks (DBN) to detect the presence of intrusion in cloud data and produce alarms, in case of presence of intrusions. Equilibrium Optimizer Algorithm (EOA) is used to fine tune the hyperparameters in DBN model. A detailed set of simulations was conducted on benchmark datasets and the analysis results were compared. A detailed comparison was conducted for various models to satisfy the security requirements of cloud network and the results established the supremacy of the proposed ODBN-IDS model. [ABSTRACT FROM AUTHOR]