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.

翻译：基于人工智能（AI）的入侵检测系统（IDS）被视为主动检测复杂网络中新型攻击的潜在机制。尽管现有综述论文采用系统综述或简单方法对异常网络入侵检测系统（NIDS）相关工作进行梳理与批判，但目前的方法仍以定量描述的传统方式，通过综合与总结数据对比来发现现有不足，而未考虑算法性能。本文对面向网络入侵检测系统（NIDS）的人工智能方法进行了系统性与元分析研究，重点聚焦于网络安全中的深度学习（DL）与机器学习（ML）方法。文中阐释了深度学习算法的结构，并基于网络基础设施与攻击类型验证了数据入侵网络的合理性。通过实施元分析，并从有效性、所用数据集、检测攻击类型、分类任务及时间复杂度等维度讨论DL与ML方法的验证过程，我们对当前基于NIDS的学术出版物进行了系统性基准评估。所提方法被应用于审查基于异常的NIDS（anomaly-NIDS）模型。此外，本文探讨了所提算法与选定数据集的有效性，并针对机器学习与深度学习在NIDS领域的最新发展方向与改进措施进行论述。多项研究还着重指出了在新型网络攻击持续演变的背景下，改进异常入侵检测系统（anomaly-IDS）以实现持续检测的未来趋势。