Performance and accuracy trade-off analysis of techniques for anomaly detection in IoT sensors

IoT environments are typically composed of hundreds of geographically distributed sensors. Usually, these sensors are not physically protected from unauthorized access, which makes them vulnerable to exploitation where they can be manipulated to send incorrect data. The identification of such compromised sensors can be helpful in the process of exclusion or verification by administrators. To perform the detection of anomalous sensors, several algorithms can be used. However, based on the algorithm used, this evaluation may be delayed or can be inaccurate. Therefore, to detect sensors with different behavior compared to others, we evaluated the trade-off between performance and accuracy of different anomalies detection algorithms. The results showed that Mahalanobis Distance could improve the trade-off between detecting multiple anomalous sensors at execution time and accuracy to avoid false-positives.