A Secure Intrusion Detection System in Cyberphysical Systems Using a Parameter-Tuned Deep-Stacked Autoencoder

Cyber physical systems (CPSs) are a networked system of cyber (computation, communication) and physical (sensors, actuators) elements that interact in a feedback loop with the assistance of human interference. Generally, CPSs authorize critical infrastructures and are considered to be important in the daily lives of humans because they form the basis of future smart devices. Increased utilization of CPSs, however, poses many threats, which may be of major significance for users. Such security issues in CPSs represent a global issue; therefore, developing a robust, secure, and effective CPS is currently a hot research topic. To resolve this issue, an intrusion detection system (IDS) can be designed to protect CPSs. When the IDS detects an anomaly, it instantly takes the necessary actions to avoid harming the system. In this study, we introduce a new parameter-tuned deep-stacked autoencoder based on deep learning (DL), called PT-DSAE, for the IDS in CPSs. The proposed model involves preprocessing, feature extraction, parameter tuning, and classification. First, data preprocessing takes place to eliminate the noise present in the data. Next, a DL-based DSAE model is applied to detect anomalies in the CPS. In addition, hyperparameter tuning of the DSAE takes place using a search-and-rescue optimization algorithm to tune the parameters of the DSAE, such as the number of hidden layers, batch size, epoch count, and learning rate. To assess the experimental outcomes of the PT-DSAE model, a series of experiments were performed using data from a sensor-based CPS. Moreover, a detailed comparative analysis was performed to ensure the effective detection outcome of the PT-DSAE technique. The experimental results obtained verified the superior performance on the applied data over the compared methods.