Distributed and explainable GHSOM for anomaly detection in sensor networks
Paolo Mignone,
Roberto Corizzo,
Michelangelo Ceci
Abstract:The identification of anomalous activities is a challenging and crucially important task in sensor networks. This task is becoming increasingly complex with the increasing volume of data generated in real-world domains, and greatly benefits from the use of predictive models to identify anomalies in real time. A key use case for this task is the identification of misbehavior that may be caused by involuntary faults or deliberate actions. However, currently adopted anomaly detection methods are often affected by… Show more
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