Intelligent Forensic Investigation Using Optimal Stacked Autoencoder for Critical Industrial Infrastructures

Industrial Control Systems (ICS) can be employed on the industrial processes in order to reduce the manual labor and handle the complicated industrial system processes as well as communicate effectively. Internet of Things (IoT) integrates numerous sets of sensors and devices via a data network enabling independent processes. The incorporation of the IoT in the industrial sector leads to the design of Industrial Internet of Things (IIoT), which find use in water distribution system, power plants, etc. Since the IIoT is susceptible to different kinds of attacks due to the utilization of Internet connection, an effective forensic investigation process becomes essential. This study offers the design of an intelligent forensic investigation using optimal stacked autoencoder for critical industrial infrastructures. The proposed strategy involves the design of manta ray foraging optimization (MRFO) based feature selection with optimal stacked autoencoder (OSAE) model, named MFROFS-OSAE approach. The primary objective of the MFROFS-OSAE technique is to determine the presence of abnormal events in critical industrial infrastructures. The MFROFS-OSAE approach involves several subprocesses namely data gathering, data handling, feature selection, classification, and parameter tuning. Besides, the MRFO based feature selection approach is designed for the optimal selection of feature subsets. Moreover, the OSAE based classifier is derived to detect abnormal events and the parameter tuning process is carried out via the coyote optimization algorithm (COA). The performance validation of the MFROFS-OSAE technique takes place using the benchmark dataset and the experimental results reported the betterment of the MFROFS-OSAE technique over the recent approaches interms of different measures.