Advanced AI-Powered Intrusion Detection Systems in Cybersecurity Protocols for Network Protection
Hari Mohan, Aditya Pal, Rashidov Akbar Ergash o’g’li, Bobokhonov Akhmadkhon Kholmirzokhon Ugli, Yarmatov Sherzojon Shokirovich
Abstract
Conventional rule-based network intrusion detection systems (NIDS) find it difficult to remain with the increasing complexity of cyber-attacks. To solve these issues, this study examines the development of NIDS as well as the transformative potential of artificial intelligence (AI). AI-enhanced NIDS can efficiently identify and respond to known and unknown threats in real-time by utilizing machine learning (ML) techniques. The system can differentiate between typical network behavior and abnormalities using both supervised and unsupervised learning techniques, as opposed to depending exclusively on pre-established rules. The accuracy and adaptability of the system are further improved by deep learning (DL) architectures like recurrent neural networks (RNNs) and convolutional neural networks (CNNs). The paper explores the past developments of intrusion detection, comparing rule-based approaches to modern AI-driven systems. It discusses cutting-edge techniques like anomaly detection, ensemble methods, and hybrid models. While Recognizing issues such as adversarial attacks and interpretability, the article underlines the importance of AI-enhanced NIDS in protecting digital infrastructure. This study provides a complete overview, unique insights, and practical advice for cybersecurity experts looking to install and optimize AI-powered intrusion detection solutions.