Abstract
Network Anomaly Detection Systems (NADSs) are gaining a more important role in most network defense systems for detecting and preventing potential threats. The paper discusses various aspects of anomaly-based Network Intrusion Detection Systems (NIDSs). The paper explains cyber kill chain models and cyber-attacks that compromise network systems. Moreover, the paper describes various Decision Engine (DE) approaches, including new ensemble learning and deep learning approaches. The paper also provides more details about benchmark datasets for training and validating DE approaches. Most of NADSs’ applications, such as Data Centers, Internet of Things (IoT), as well as Fog and Cloud Computing, are also discussed. Finally, we present several experimental explanations which we follow by revealing various promising research directions.
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