Evaluating Machine Learning Algorithms for Enhancing Network Intrusion Detection Systems: A Comparative Study

Network Intrusion Detection Systems (NIDS) play crucial role for maintaining the security of modern network infrastructures. With the increasing sophistication of cyber-attacks, traditional signature-based methods have become insufficient. Machine Learning (ML) techniques offer a promising alternative, providing the ability to detect novel threats by learning from network traffic patterns. This article provides a comparative analysis of different ML algorithms applied to NIDS, evaluating their performance based on key metrics such as accuracy, precision, recall, and Fl-score.