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Intrusion detection systems (IDSs) built on artificial intelligence (AI) arepresented as latent mechanisms for actively detecting fresh attacks over acomplex network. Although review papers are used the systematic review orsimple methods to analyse and criticize the anomaly NIDS works, the currentreview uses a traditional way as a quantitative description to find currentgaps by synthesizing and summarizing the data comparison without consideringalgorithms performance. This paper presents a systematic and meta-analysisstudy of AI for network intrusion detection systems (NIDS) focusing on deeplearning (DL) and machine learning (ML) approaches in network security. Deeplearning algorithms are explained in their structure, and data intrusionnetwork is justified based on an infrastructure of networks and attack types.By conducting a meta-analysis and debating the validation of the DL and MLapproach by effectiveness, used dataset, detected attacks, classification task,and time complexity, we offer a thorough benchmarking assessment of the currentNIDS-based publications-based systematic approach. The proposed method isconsidered reviewing works for the anomaly-based network intrusion detectionsystem (anomaly-NIDS) models. Furthermore, the effectiveness of proposedalgorithms and selected datasets are discussed for the recent direction andimprovements of ML and DL to the NIDS. The future trends for improving ananomaly-IDS for continuing detection in the evolution of cyberattacks arehighlighted in several research studies.

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