| Attacks Detection in Industrial Cyber-Physical Systems Using Convolutional Neural Networks |
| Paper ID : 1060-ICEEM2023 (R1) |
| Authors: |
|
Mohamed Salah Elhabasha *, Lamiaa Mohamed Elshenawy, Mohamed Hamdy Mohamed Department of Industrial Electronics and Control Engineering, Faculty of Electronic Engineering, Menoufia University, Menouf, Egypt |
| Abstract: |
| Cyber-physical systems (CPSs) are widely used and extremely important due to their promise for substantial and long-term benefits to society, economy, environment, and human life. Moreover, the development in communication, computing, and storage technologies has resulted in a revolution in information communication technologies (ICT). The utilization of CPSs in industrial control systems are well known as industrial CPSs (ICPSs). Consequently, these systems have become a popular target for cyber-attacks and malicious threats which can disable the system’s functioning and have serious safety-related consequences. This paper presents an attack detection method based on simple neural networks, 1D convolutional neural networks. The presented method is verified using a popular public dataset, the Secure Water Treatment testbed (SWaT), which is a small-scale representation of a real-world industrial water treatment plant. The results have demonstrated the effectiveness of the presented method for attack detection in ICPSs. |
| Keywords: |
| Industrial cyber physical systems (ICPS), Cyberattacks detection, Convolutional neural networks, Industrial control systems. |
| Status : Paper Accepted |
