Sentinel AI: Revolutionizing Cybersecurity with Intelligent Intrusion Detection

Abstract

The research paper evaluates the limitations of the Analysis of Host-Based and Network-Based Intrusion Detection System article regarding open-source host-based intrusion detection systems OSSEC and Snort while developing and presenting an AI-based intrusion detection system that improves detection accuracy, reduces false positives, and supports scalability. This paper introduces an AI-based IDS system that analyses existing host- and network-based IDS systems to find their missing elements. The training system requires a different network and hosts behavioural patterns through SVC and Decision trees, Logistic regression, and machine learning algorithms. The famous NLSKDDCUP99 Dataset is used. The AI-driven IDS produces errorless attack detection outcomes without generating any erroneous alerts. The study submits aid through recommendations, which conclude with a proposal that AI hardware should strengthen intrusion detection systems to protect cybersecurity operations.