Detection of Cyber Attack and Network Attack Using Machine Learning

This study delves into the escalating challenge of cybercrime by highlighting the limitations of current intrusion detection systems (IDS) and the emergent importance of machine learning in bolstering cyber security. Focused on enhancing detection capabilities, the research proposes a novel approach utilizing deep neural networks within a leader-follower framework to identify attacks early in their initiation. Moreover, the study explores resilient control algorithms and reputation mechanisms to isolate and neutralize malicious agents within complex cyber-physical systems (CPS). Experimentation showcases the superior efficacy of deep learning algorithms in detecting threats compared to conventional methods, paving the way for a more proactive, cost-effective, and efficient cyber security landscape.