Meta‐analysis and systematic review for anomaly network intrusion detection systems: Detection methods, dataset, validation methodology, and challenges

Intrusion detection systems built on artificial intelligence (AI) are presented as latent mechanisms for actively detecting fresh attacks over a complex network. The authors used a qualitative method for analysing and evaluating the performance of network intrusion detection system (NIDS) in a systematic way. However, their approach has limitations as it only identifies gaps by analysing and summarising data comparisons without considering quantitative measurements of NIDS's performance. The authors provide a detailed discussion of various deep learning (DL) methods and explain data intrusion networks based on an infrastructure of networks and attack types. The authors’ main contribution is a systematic review that utilises meta‐analysis to provide an in‐depth analysis of DL and traditional machine learning (ML) in notable recent works. The authors assess validation methodologies and clarify recent trends related to dataset intrusion, detected attacks, and classification tasks to improve traditional ML and DL in NIDS‐based publications. Finally, challenges and future developments are discussed to pose new risks and complexities for network security. The authors offer a systematic meta‐analysis of AI applications in network intrusion detection systems (NIDS) with a specific focus on deep learning (DL) and machine learning (ML) techniques within the domain of network security. Through a comprehensive meta‐analysis and rigorous evaluation of the effectiveness, dataset utilisation, attack detection capabilities, classification tasks, and time complexity of DL and ML approaches, the authors present a comprehensive benchmarking assessment of the prevailing systematic approach in NIDS‐based publications.