An Intelligent Intrusion Detection System for Network Security Using Machine Learning - Rajabboyeva Surayyo; Samarov Xusnutdin; Azizbek Xaitbayev Pirnazarovich; Allanazarov Asadbek Azatovich (NAZARIY VA AMALIY FANLARDAGI USTUVOR ISLOHOTLAR VA ZAMONAVIY TA'LIMNING INNOVATSION YO'NALISHLARI, 2025)
As cyber threats become increasingly sophisticated, the need for intelligent and adaptive security mechanisms has become more crucial than ever. Traditional intrusion detection systems (IDS) struggle to identify novel or evolving attacks due to their reliance on static rules and signatures. This research proposes a machine learning-based IDS designed to detect anomalous behavior in network traffic. Using supervised and unsupervised learning models such as Random Forest, Support Vector Machine (SVM), and Autoencoders, the system is trained and tested on benchmark datasets including NSL-KDD and CICIDS2017. The proposed IDS demonstrates high accuracy and robustness in identifying multiple types of attacks and offers a real-time detection interface for practical deployment.