Accurate Attack Detection in Intrusion Detection System for cyber threat intelligence feeds using machine learning techniques.

With the advancement of modern technology, cyber-attacks are continuously rising. Malicious behavior in the network is discovered using security devices like intrusion detection systems (IDS), firewalls, and antimalware systems. To defend organizations, procedures for detecting threats more correctly and precisely must be defined. The proposed study investigates the significance of cyber-threat intelligence (CTI) feeds in accurate IDS detection. The NSL-KDD and CSE-CICIDS-2018 datasets were analyzed in this study. This research makes use of normalization, transformation, and feature selection algorithms. Machine learning (ML) techniques were employed to determine if the traffic was normal or an attack. With the proposed study the ability to identify network attacks has improved using machine learning algorithms. The proposed model provides 98% accuracy, 97% precision, and 96% recall respectively.