Detection Of Cyber Attack Using Artificial Intelligence

Cyber-physical systems (CPS) have revolutionized many industries by integrating physical processes, computer resources, and communication capabilities. However, the increasing connectivity and complexity of these systems have made them vulnerable to cyberattacks, including spoof attacks that distort data or inject false information into the system. Such attacks can lead to performance issues or complete system failure if not detected and addressed promptly. To address this challenge, machine learning techniques such as deep learning algorithms are being applied to simplify data processing and uncover hidden patterns that may indicate a cyberattack. In this context, a deep neural network’s structure is suggested for the detection phase, which must alert the system to the attack’s presence in its early stages.Furthermore, robust control techniques have been studied to isolate the offending agent in the leader-follower system. The control system utilizes a reputation algorithm to isolate the misbehaving agent following the attack detection phase using a deep neural network.Experimental studies have shown that deep learning algorithms can detect threats more effectively than traditional approaches, making cybersecurity easier, more proactive, less expensive, and significantly more successful. Overall, it is crucial to ensure the security of CPS to ensure their continued success and development in various industries.