Meta-Analysis and Systematic Review for Anomaly Network Intrusion Detection Systems: Detection Methods, Dataset, Validation Methodology, and Challenges

TL;DR

This paper conducts a systematic meta-analysis of AI-based network intrusion detection systems, focusing on deep learning and machine learning approaches, datasets, validation methods, and challenges to identify gaps and future directions.

Contribution

It provides a comprehensive benchmarking and critical review of current anomaly-based NIDS research, highlighting performance, datasets, and validation methodologies.

Findings

Deep learning algorithms are effectively explained and analyzed.

Current datasets and validation methods are critically assessed.

Future trends and challenges in anomaly-NIDS are discussed.

Abstract

Intrusion detection systems (IDSs) built on artificial intelligence (AI) are presented as latent mechanisms for actively detecting fresh attacks over a complex network. Although review papers are used the systematic review or simple methods to analyse and criticize the anomaly NIDS works, the current review uses a traditional way as a quantitative description to find current gaps by synthesizing and summarizing the data comparison without considering algorithms performance. This paper presents a systematic and meta-analysis study of AI for network intrusion detection systems (NIDS) focusing on deep learning (DL) and machine learning (ML) approaches in network security. Deep learning algorithms are explained in their structure, and data intrusion network is justified based on an infrastructure of networks and attack types. By conducting a meta-analysis and debating the validation of the DL and ML approach by effectiveness, used dataset, detected attacks, classification task, and time complexity, we offer a thorough benchmarking assessment of the current NIDS-based publications-based systematic approach. The proposed method is considered reviewing works for the anomaly-based network intrusion detection system (anomaly-NIDS) models. Furthermore, the effectiveness of proposed algorithms and selected datasets are discussed for the recent direction and improvements of ML and DL to the NIDS. The future trends for improving an anomaly-IDS for continuing detection in the evolution of cyberattacks are highlighted in several research studies.