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Anomaly Detection under Constraints

Anomaly detection approaches are used in many problems of IT Security, such as malware detection, access control and authentication. Machine learning methods of anomaly detection are used in case that rule-based or heuristic systems do not satisfy the needs to analyze statistically variable data. Very often anomaly detection approaches need to be executed on resource-constrained devices, such as mobile phones, routers and similar. There we encounter constraints in resources: memory, bandwidth, power, CPU. We develop and test adaptive machine learning methods to optimize anomaly detection in this setting.

Researcher: Bojan Kolosnjaji