“Review of Intrusion Detection System Using AI”

- The increasing dependence on computer networks and internet-based services has made information systems more vulnerable to cyber attacks and unauthorized access. Conventional security mechanisms are often limited in their ability to identify complex and evolving threats in real time. An Intrusion Detection System (IDS) is an essential component of network security that monitors system activities and network traffic to detect potential security breaches. This research paper presents a comprehensive review of Intrusion Detection Systems (IDS) using Artificial Intelligence (AI) techniques to enhance the detection capability of modern security systems. Various machine learning algorithms discussed in the literature analyze network traffic patterns and distinguishes between normal and suspicious behavior. The AI-based model is trained using historical network data, allowing it to recognize hidden patterns associated with malicious activities. The reviewed studies indicate that AI-based approaches can improve detection accuracy, reduce false alarms, and identify both known and unknown attacks effectively. The integration of artificial intelligence with intrusion detection mechanisms provides a more adaptive and intelligent solution for protecting network infrastructures. The study highlights how AI-driven IDS can contribute to strengthening cybersecurity by enabling faster and more reliable threat detection in dynamic network environments.