IntelliSecure AI-Powered Intrusion Detection Framework

The rapid advancement of technology has led to the emergence of AI-driven security systems that mimic human behavior, particularly in detecting intrusions. Security remains a paramount concern in the interconnected society, with Intrusion Detection Systems (IDS) being crucial for identifying various categories of attacks. This study explores the utilization of machine learning (ML) techniques in intrusion detection for networks, focusing on recent strategies and approaches employed in Network-centric Systems. Through analysis of recent research, we highlight the typical attacks detected by these systems and the challenges they face. The primary objective of this study is to detect attacks based on specific patterns in the flow of information across the internet. AI-based intrusion detection systems play a critical role in combating malware and hacking, ensuring the confidentiality of sensitive data within organizations. These systems are designed to address real-world challenges, aiding in the prevention of network misuse and the identification of web vulnerabilities. Utilizing methods such as machine learning, decision trees, and fuzzy semantics, this intrusion detection system offers a comprehensive approach to enhancing cybersecurity.