Intrusion Detection in Network Systems using AI
DOI : 10.46335/IJIES.2025.10.10.1
Authors: Pradip Athawale, Dr. S.V Sonekar, Dr. Supriya S. Sawwashere, Dr. Ashutosh O. Lanjewar, Prof. Mirza M. Baig, Dr. S.K. Mandavgade
Abstract: There essential to protecting network infrastructures by identifying and addressing potential cyber threats. However, with the continuous evolution and increasing complexity of cyber-attacks, traditional IDS methods face significant limitations, particularly in identifying new and sophisticated threats. Techniques emerged as a promising solution have shown significant improvements adaptability, detection accuracy, and scalability. Recent advancements in the field include the use of federated learning to develop privacy-preserving IDS models and the exploration of quantum computing to accelerate the training of AI algorithms. This review explores the transformative impact of AI on IDS, underscoring its potential to overcome the limitations of traditional approaches. It also discusses emerging research directions aimed at building more secure, adaptable, and scalable IDS solutions to counteract of modern cyber disstruction.Relevant terms: Intrusion Detection System, Network Security, Artificial Intelligence, Machine Learning, Deep Learning, Cyber security.
