Deep learning enabled class imbalance with sand piper optimization based intrusion detection for secure cyber physical systems

A cyber physical system (CPS) is a network of cyber (computation, communication) and physical (sensors, actuators) components which interact with one another in a feedback form with human intervention. CPS authorizes the critical infrastructure and is treated as essential in day to day life as they exist in the basis of future smart devices. The increased exploitation of the CPS results in various threats and becomes a global problem. Therefore, it becomes essential to develop a safe, efficient, and robust CPS for real tine environment. For resolving this problem and accomplish security in CPS environment, intrusion detection system (IDS) can be developed. This study introduces an imbalanced generative adversarial network (IGAN) with optimal kernel extreme learning machine (OKELM), called IGAN-OKELM technique for intrusion detection in CPS environment. The proposed IGAN-OKELM technique mainly aims to address the class imbalance problem and intrusion detection. Besides, the IGAN-OKELM technique involves the IGAN model handling the class imbalance problem by the use of imbalanced data filter and convolution layers to the conventional generative adversarial network (GAN), which generates new instances for minority class labels. Moreover, the OKELM model is applied as a classifier and the optimal parameter tuning of the KELM model is performed by the use of sand piper optimization (SPO) algorithm and thereby improvises the intrusion detection performance. A wide ranging simulation analysis is carried out using benchmark dataset and the results are examined under varying aspects. The experimental results reported the better performance of the IGAN-OKELM technique over the recent state of art approaches interms of different measures.