A COMPREHENSIVE STUDY OF ARTIFICIAL INTELLIGENCE APPLICATIONS IN INTRUSION DETECTION AND PREVENTION
Siranjeevi Srinivasa Raghavan · 2025
The integration of artificial intelligence (AI) into Intrusion Detection and Prevention Systems (IDPS) has significantly transformed the field of cybersecurity.AIdriven IDPS leverage advanced techniques such as machine learning, deep learning, and natural language processing to detect, analyze, and mitigate sophisticated cyber threats.This study explores the applications of AI in IDPS, highlighting its ability to enhance anomaly detection, behavioral analysis, and real-time threat prevention.It also examines the challenges associated with AI adoption, including ethical concerns, privacy issues, scalability, and real-time processing requirements.The findings underscore the effectiveness of AI-based IDPS compared to traditional methods, particularly in handling zero-day attacks and advanced persistent threats.Furthermore, emerging trends such as federated learning, graph neural networks, and adaptive security frameworks suggest a promising future for AI in cybersecurity.This research provides valuable insights into the development and deployment of AI-driven IDPS while emphasizing the need for ethical considerations and privacy-preserving techniques.By addressing these challenges and leveraging emerging innovations, AIbased IDPS can play a pivotal role in ensuring robust network security in an increasingly interconnected world.