A Novel Approach to Network Forensic Analysis: Combining Packet Capture Data and Social Network Analysis

Log data from computers used for network forensic analysis is ineffective at identifying specific security threats. Log data limitations include the difficulty in reconstructing communication patterns between nodes and the inability to identify more advanced security threats. By combining traditional log data analysis methods with a more effective combination of approaches, a more comprehensive view of communication patterns can be achieved. This combined approach can then help identify potential security threats more effectively. It's difficult to determine the specific benefits of combining Packet Capture (PCAP) and Social Network Analysis (SNA) when performing forensics. This article proposes a new approach to forensic analysis that combines PCAP and social network analysis to overcome some of the limitations of traditional methods. The purpose of this discovery is to improve the accuracy of network forensic analysis by combining PCAP and social network analysis to provide a more comprehensive view of network communication patterns. Network forensics, which combines pcap analysis and social network analysis, provides more comprehensive results. PCAP analysis is used to analyze network traffic, conversation statistics, protocol distribution, packet content and round-trip times. Social network analysis maps communication patterns between nodes and identifies the most influential key players within the network. PCAP analysis efficiently captures and analyzes network packets, and SNA provides insight into relationships and communication patterns between devices on the network.

@article{Sembiring2023,
  title     = {A Novel Approach to Network Forensic Analysis: Combining Packet Capture Data and Social Network Analysis},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {14},
  number    = {3},
  year      = {2023},
  publisher = {The Science and Information Organization},
  author    = {Irwan Sembiring and Suharyadi and Ade Iriani and Jenni Veronika Br Ginting and Jusia Amanda Ginting},
  doi       = {10.14569/IJACSA.2023.0140353},
  url       = {https://doi.org/10.14569/IJACSA.2023.0140353}
}