Machine Learning Techniques for Intelligent Vulnerability Detection in Cyber-Physical Systems
Shagun Sharma, Kalpna Guleria · 2022 International Conference on Data Analytics for Business and Industry (ICDABI) · 2022
Cyber-Physical Systems (CPSs) are crucial to industry 4.0 because they provide remote access and control of equipment, machinery, and systems by using the internet. This kind of control has the ability to change the world since it is effective in terms of communication, computing, actuators, and sensors. The CPS framework is quite complicated because it requires a lot of different components to finish its various jobs. Due to their critical nature, severe system damage, safety issues, and vulnerable behavior, cyberattacks can happen. According to a study, social animals are now more worried about their safety due to the rise in CPSs assaults. To identify attacks against CPSs, access control techniques and cryptography are specifically used. The rise in attackers has made it more challenging to prevent cyber-attacks completely, necessitating the requirement for an efficient method of CPS security. The use of machine learning and deep learning algorithms can protect CPSs from intrusions, deception, and severe harm. This article provides a thorough overview of machine learning and deep learning methods used in various CPS interactions. This paper presents the most effective algorithm for detecting CPS attacks and identifies SVM as the outperforming algorithm with the highest accuracy of 97.5%, which is quite good compared to other potential solutions. A dataset made up of 144 data points of CPS attacks, namely, replay attack, packet dropped failure, modification of information along with low latency failure was used in the study, and it was further divided into training and testing the model in an 80:20 ratio. This report also aids academicians and researchers in comprehending cyberattacks and the tools and algorithms used to combat them.