Solving the IoT's "Boy Who Cried Wolf" Problem: From Individual Alerts to Cumulative Certainty

2. Related Work

The problem of anomaly detection has been studied in many different application domains and many techniques have been proposed, based on statistical inference, data mining, signal processing, and recently machine learning, among others. We note that in the literature of anomaly detection, anomalies have been classified into three categories: point anomaly, contextual anomaly, and collective anomaly [5]. However, they are all concerned with the detection of individual anomalous events, which are different from the cumulative anomaly we consider in this paper. In cumulative anomaly we are more concerned with the cause of anomalous events (for example, compromised IoT device), instead of individual anomalous events. As a consequence, we need to accumulate sufficient evidence (individual anomalous events) to reach a conclusion (for example, if an IoT device is compromised) in cumulative anomaly detection.

Given the importance of improving IoT security, many security attack ...