Insider attack detection in cloud

What is it about?

Traditional access control systems and anomaly detection frameworks developed till date are not adequate to prevent and detect malicious insider activities in Cloud Collaboration Systems (CCS). Most of the systems aim at detecting malicious insider attacks of a single user. But there is a need to detect malicious dynamic behaviors of users to build robust systems. Considering these limitations, the main motivation of this paper is to timely detect such anomalous behaviors of users in CCS. Thus we propose an anomaly detection framework called Sliding Window based Anomaly Detection using maximum mean Discrepancy (SWAD-MMD).

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