Enhancing Insider Threat Detection Using Advanced IDPS

Intrusion Detection Prevention Systems (IDPS) components are very important but insufficient to deal with insider threats, even if it is at the base of the database. Data theft is found quite commonly at this place, and that makes this task important. This research discusses the latest algorithms and techniques to improve the IDPS for detecting and preventing insider threats. One such advanced method utilized is Isolation Forest, LSTM networks, Data Packet Inspection (DPI), and dynamic adjustment, which could improve the accuracy of the system, in turn adapting it for needed solutions. Unlike traditional networks, this approach including advanced techniques with User and Entity Behaviour Analytics (UEBA) and blockchain technology for immutable logging, indicates a comprehensive approach that will detect anomalies in user behavior and communications encrypted in nature.