A Review of Enhancing Intrusion Detection Systems for Cybersecurity Using Artificial Intelligence (AI)
Michal Markevych, Maurice Dawson
Abstract
Abstract The escalating complexity of cyber attacks demands innovative intrusion detection systems (IDS) to safeguard critical assets and data. The study aims to explore the potential of Artificial Intelligence (AI) in enhancing the IDS's ability to identify and classify network traffic and detect anomalous behavior. The paper offers a concise overview of IDS and AI and examines the existing literature on the subject, highlighting the significance of integrating advanced language models for cybersecurity enhancement. The research outlines the methodology employed to assess the efficacy of AI within IDS. Furthermore, the study considers key performance metrics such as detection accuracy, false positive rate, and response time to ensure a comprehensive evaluation. Findings indicate that AI is a valuable asset in enhancing the accuracy of AI for detecting and responding to cyber attacks. Nonetheless, the study also brings to light certain limitations and challenges associated with incorporating AI into IDS, such as computational complexity and potential biases in training data. This research emphasizes the potential of advanced language models like ChatGPT in augmenting cybersecurity solutions and offers insights into overcoming associated challenges for a more robust and effective defense against sophisticated cyber attacks.