Soceanu, Omri; Adir, Allon; Aharoni, Ehud; Greenberg, Lev; Abie, Habtamu
Security of IoT systems is a growing concern with rising risks and damages due to successful attacks. Breaches are inevitable, attacks have become more sophisticated, and securing critical infrastructure has become a greater challenge. Anomaly detection is an established approach for detecting security attacks, without relying on predefined rules or signatures of potential attacks. However, existing outlier detection techniques require adaptation if they are to be applied in a Big Data cloud context. We describe a novel outlier detection solution, which is currently being used by hundreds of customers with highly variable data scales. We describe our work in adapting this technology to handle IoT on a Big Data cloud setting. Specifically, we focus on efficient outlier analysis and managing large numbers of alerts using automatically controlled alert budgets.