Network anomaly detection systems (NADSs) play a significant role in every network defense system as they detect and prevent malicious activities. Therefore, this paper offers an exhaustive overview of different aspects of anomaly-based network intrusion detection systems (NIDSs). Additionally, contemporary malicious activities in network systems and the important properties of intrusion detection systems are discussed as well. The present survey explains important phases of NADSs, such as pre-processing, feature extraction and malicious behavior detection and recognition. In addition, with regard to the detection and recognition phase, recent machine learning approaches including supervised, unsupervised, new deep and ensemble learning techniques have been comprehensively discussed; moreover, some details about currently available benchmark datasets for training and evaluating machine learning techniques are provided by the researchers. In the end, potential challenges together with some future directions for machine learning-based NADSs are specified.

中文翻译：

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新兴技术中用于网络恶意行为检测的机器学习方法综述

网络异常检测系统（NADS）在每个网络防御系统中都扮演着重要的角色，因为它们可以检测并防止恶意活动。因此，本文全面概述了基于异常的网络入侵检测系统（NIDS）的各个方面。此外，还讨论了网络系统中的当代恶意活动以及入侵检测系统的重要属性。本调查说明了NADS的重要阶段，例如预处理，特征提取以及恶意行为检测和识别。另外，关于检测和识别阶段，已经全面讨论了包括监督，无监督，新的深度和整体学习技术在内的最新机器学习方法；而且，研究人员提供了一些有关训练和评估机器学习技术的当前可用基准数据集的详细信息。最后，针对基于机器学习的NADS指出了潜在的挑战以及未来的发展方向。