AI BASED – NETWORK INTRUSION DETECTION SYSTEM

As cyber threats become more complex and persistent, traditional security mechanisms are often unable to effectively respond. This has led many organizations to explore intelligent solutions to protect critical information and ensure secure network operations. This paper examines how Artificial Intelligence (AI), especially machine learning techniques, can strengthen Intrusion Detection Systems (IDS) by recognizing irregular activities and differentiating them from normal traffic. It discusses key IDS and AI concepts and evaluates past research on how these tools adapt to evolving threats. Experimental analysis is carried out using widely recognized datasets, including CIC-IDS 2017 and CIC-IDS 2018, to measure the performance of different machine learning models. While AI demonstrates a strong ability to identify complex attack patterns, the study also points out real-world challenges like computational overhead and training data limitations that may influence model accuracy.