Their meta-data header packets were statistically profiled to generate digital fingerprints that try to link their action on the network to their identity accurately.
A total of 95,795,379 network packet headers from 96 subjects were successfully collected.
This study utilises the decision tree classification approach to characterise users from their behavioural web navigation pattern using the meta-data of captured network packets (Destination IP, Protocol, Port Source, and Port Destination). However, suspect identification is a big challenge in network forensics due to the anonymous nature of data transmission across the network. The drastic rise in the cybercrime rate associated with the surge of users' dependence on the Internet has elevated the concern of digital forensic examiners toward the footprints of perpetrators left in a virtual environment. The properties of both hardware appliances and packet analyzer software are reviewed from the perspective of their potential use in network forensics. Considering that not all network information can be used in court, the types of digital evidence that might be admissible are detailed. This paper is a comprehensive survey of the utilization of packet analysis, including deep packet inspection, in network forensics, and provides a review of AI-powered packet analysis methods with advanced network traffic classification and pattern identification capabilities. This can be used to find traces of nefarious online behavior, data breaches, unauthorized website access, malware infection, and intrusion attempts, and to reconstruct image files, documents, email attachments, etc. Packet analysis is a primary traceback technique in network forensics, which, providing that the packet details captured are sufficiently detailed, can play back even the entire network traffic for a particular point in time.
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