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Network forensic techniques help in tracking different types of cyber attackby monitoring and inspecting network traffic. However, with the high speed andlarge sizes of current networks, and the sophisticated philosophy of attackers,in particular mimicking normal behaviour and/or erasing traces to avoiddetection, investigating such crimes demands intelligent network forensictechniques. This paper suggests a real-time collaborative network Forensicscheme (RCNF) that can monitor and investigate cyber intrusions. The schemeincludes three components of capturing and storing network data, selectingimportant network features using chi-square method and investigating abnormalevents using a new technique called correntropy-variation. We provide a casestudy using the UNSW-NB15 dataset for evaluating the scheme, showing its highperformance in terms of accuracy and false alarm rate compared with threerecent state-of-the-art mechanisms.

RCNF: Real-time Collaborative Network Forensic Scheme for Evidence Analysis