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The Cloud Security Alliance lists data theft and insider attacks as critical threats to cloud security. Our work puts forth an approach using a train, monitor, detect pattern which leverages a stateful rule based k-nearest neighbors anomaly detection technique and system state data to detect inside attacker data theft on Infrastructure as a Service (IaaS) nodes. We posit, instantiate, and demonstrate...
To secure IaaS cloud environments, multiple layers of security mechanisms must be deployed and monitored. When a threat is detected, it must be appropriately acted upon. A common problem with monitoring such approaches is the sheer volume of alarms generated. Some alerts may be false positives, others are informational. It is challenging for cloud providers to quickly interpret which events to act...
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