Cyber-Physical Systems and IoT Intrusion Detection: A Hybrid Deep Learning Perspective
S. Jaya Prakash, Devi D, Narayanaperumal Muthukumaran, R Romila · 2025
Several vital resources are increasingly being protected by cyber-physical systems (CPSs), makes the detection of incidents on these systems critical. CPSs along with other domains, such as the Internet of Things (IoT), frequently use machine learning (ML) and deep learning (DL) approaches to combat intrusion detection. Existing approaches, however, frequently favor the deployment of advanced detection models over their usefulness in actual operations. This research work develops a hybrid intrusion detection technique by combining Convolutional Neural Networks (CNN) with Long Short-Term Memory (LSTM). This work analyzes the experimental results to LSTM and CNN to develop an effective comparative approach. The experiments are conducted on the two datasets. The efficiency indicators examined are intrusion detection results such as accuracy, precision, recall, and f1score. This comparison indicates that the proposed hybrid algorithm is the preferable approach.