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