Statistical Behavior of Packet Counts for Network Intrusion Detection

Intrusions and attacks have become a very serious problem in network world. This paper presents a statistical characterization of packet counts that can be used for network intrusion detection. The main idea is based on detecting any suspicious behavior in computer networks depending on the comparison between the correlation results of control and data planes in the presence and absence of attacks using histogram analysis. Signal processing tools such as median filtering, moving average filtering, and local variance estimators are exploited to help in developing network anomaly detection approaches. Therefore, detecting dissimilarity can indicate an abnormal behavior.