Authors - Aarya Sagar Sonawane, Rutuja Rajendra Thorwat, Shravani Rajeev Deshpande, A. R. Bankar Abstract - A significant security issue facing organizations is insider threats since one has access to privileged information and the behavior of users keeps evolving. Current solutions can be un-explainable, unable to manage new behavior patterns, generate high false positives, and un privacy friendly because of centralized data analysis. To solve these problems, this paper presents EXPLAIN-ITD, an explainable, adaptive and privacy-aware artificial intelligence system to detect insider threats. The framework is an integration of multi-modal data fusion, dual memory continuous learning, explainable risk scoring, human feedback in the loop and federated learning and differential privacy. As the exper imental findings have demonstrated, EXPLAIN-ITD has a better level of accuracy in detection, a lower level of false alarms and better interpreta bility than the current approaches.
Open Zoom