Network forensics based on fuzzy logic and expert system

Network forensics is a research area that finds the malicious users by collecting and analyzing the intrusion or infringement evidence of computer crimes such as hacking. In the past, network forensics was only used by means of investigation. However, nowadays, due to the sharp increase of network traffic, not all the information captured or recorded will be useful for analysis or evidence. The existing methods and tools for network forensics show only simple results. The administrators have difficulty in analyzing the state of the damaged system without expert knowledge. Therefore, we need an effective and automated analyzing system for network forensics. In this paper, we firstly guarantee the evidence reliability as far as possible by collecting different forensic information of detection sensors. Secondly, we propose an approach based on fuzzy logic and expert system for network forensics that can analyze computer crimes in network environment and make digital evidences automatically. At the end of the paper, the experimental comparison results between our proposed method and other popular methods are presented. Experimental results show that the system can classify most kinds of attack types (91.5% correct classification rate on average) and provide analyzable and comprehensible information for forensic experts.