Statistical Similarity of Critical Infrastructure Network Traffic Based on Nearest Neighbor Distances

Industrial control systems (ICSs) operate a variety of critical infrastructures such as waterworks and power plants using cyber physical systems (CPSs). Abnormal or malicious behavior in these critical infrastructures can pose a serious threat to society. ICS networks tend to be configured such that specific tasks are performed repeatedly. Further, for a specific task, the resulting pattern in the ICS network traffic does not vary significantly. As a result, most traffic patterns that are caused by tasks that are normally performed in a specific ICS have already occurred in the past, unless the ICS is performing a completely new task. In such environments, anomaly-based intrusion detection system (IDS) can be helpful in the detection of abnormal or malicious behaviors. An anomaly-based IDS learns a statistical model of the normal activities of an ICS. We use the nearest-neighbor search (NNS) to learn patterns caused by normal activities of an ICS and identify anomalies. Our method learns the normal behavior in the overall traffic pattern based on the number of network packets transmitted and received along pairs of devices over a certain time interval. The method uses a geometric noise model with lognormal distribution to model the randomness on ICS network traffic and learns solutions through cross-validation on random samples. We present a fast algorithm, along with its theoretical time complexity analysis, in order to apply our method in real-time on a large-scale ICS. We provide experimental results tested on various types of large-scale traffic data that are collected from real ICSs of critical infrastructures.