Traffic Flow Analysis in Digital Forensics: Unveiling Patterns and Anomalies

The rapid advancement of digital technology and the increasing interconnection of systems has resulted in a complex network of digital activities. In this scene, examining organization traffic stream has arisen as a vital device in computerized criminology. This paper dives into the “Network Traffic Flow Analysis in the Field of Digital Forensics and Cybersecurity: Revealing Patterns and Deviations” domain. The objective of the study is to investigate the way in which traffic stream examination fills in as a strong procedure for unraveling the multifaceted computerized effects left behind by clients, gadgets, and potential danger entertainers. Forensic Data analysts acquire the ability to detect security breaches, data exfiltration, or unauthorized access by closely examining examples of information transmission, source-objective connections, and correspondence conventions within an organization. This analysis allows them to identify both regular patterns and irregular deviations. Methodologies for capturing and recording network packets, the role of flow data in summarizing complex interactions, and the use of intrusion detection systems (IDS) and intrusion prevention systems (IPS) to monitor real-time network activities are all examined in this paper. Also, the review dives into utilizing AI and man-made reasoning methods for prescient investigation and inconsistency identification inside network traffic.