This paper presents a comprehensive review of Network Intrusion Detection and Prevention Systems (NIDS/NIPS). The study analyzes the evolution of intrusion detection techniques from traditional signature-based systems to modern machine learning and deep learning approaches. Various datasets, detection methodologies, and evaluation metrics used in IDS research are discussed. The paper also highlights key challenges such as encrypted traffic analysis, scalability issues, and adversarial attacks. Finally, emerging research directions and future opportunities for intelligent and adaptive intrusion detection systems are presented.
Rana et al. (Thu,) studied this question.