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Machine Learning (ML) approaches have been used to enhance the detectioncapabilities of Network Intrusion Detection Systems (NIDSs). Recent work hasachieved near-perfect performance by following binary- and multi-class networkanomaly detection tasks. Such systems depend on the availability of both(benign and malicious) network data classes during the training phase. However,attack data samples are often challenging to collect in most organisations dueto security controls preventing the penetration of known malicious traffic totheir networks. Therefore, this paper proposes a Deep One-Class (DOC)classifier for network intrusion detection by only training on benign networkdata samples. The novel one-class classification architecture consists of ahistogram-based deep feed-forward classifier to extract useful network datafeatures and use efficient outlier detection. The DOC classifier has beenextensively evaluated using two benchmark NIDS datasets. The resultsdemonstrate its superiority over current state-of-the-art one-class classifiersin terms of detection and false positive rates.

DOC-NAD: A Hybrid Deep One-class Classifier for Network Anomaly Detection