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Over the last three decades, Network Intrusion Detection Systems (NIDSs), particularly, Anomaly Detection Systems (ADSs), have become more significant in detecting novel attacks than Signature Detection Systems (SDSs). Evaluating NIDSs using the existing benchmark data sets of KDD99 and NSLKDD does not reflect satisfactory results, due to three major issues: (1) their lack of modern low footprint attack styles, (2) their lack of modern normal traffic scenarios, and (3) a different distribution of training and testing sets. To address these issues, the UNSW-NB15 data set has recently been generated. This data set has nine types of the modern attacks fashions and new patterns of normal traffic, and it contains 49 attributes that comprise the flow based between hosts and the network packets inspection to discriminate between the observations, either normal or abnormal. In this paper, we demonstrate the complexity …