<![CDATA[This study investigates the application of Snort as an intrusion detection tool for addressing insider threats in network environments. The primary focus is on utilizing network forensic techniques to identify, analyze, and respond to suspicious activities conducted by internal users. The analysis method involves implementing Snort on a Linux platform to monitor network traffic in real-time and collect digital evidence that can be used in forensic investigations. The study successfully demonstrates that Snort is effective in detecting suspicious behavior patterns associated with insider threats, such as unauthorized access attempts and potentially malicious application usage. The digital evidence gathered by Snort aids in further forensic analysis, assisting in the identification of threat sources and facilitating a swift and appropriate response to security incidents. The discussion highlights the strengths and weaknesses of using Snort in the context of insider threat detection, emphasizing the importance of meticulous configuration and regular maintenance for optimal performance. The study concludes that employing Snort within a network forensic framework enhances an organization's ability to detect, analyze, and respond to insider threats, providing better protection for organizational assets and information from various internal cyber threats. This research lays the foundation for developing more effective security policies and improving cybersecurity awareness within organizations.]]>
