Anomaly detection in embedded systems

By employing fault tolerance, embedded systems can withstand both intentional and unintentional faults. Many fault tolerance mechanisms are invoked only after a fault has been detected by whatever fault-detection mechanism is used; hence, the process of fault detection must itself be dependable if the system is expected to be fault-tolerant. Many faults are detectable only indirectly as a result of performance disorders that manifest as anomalies in monitored system or sensor data. Anomaly detection, therefore, is often the primary means of providing early indications of faults. As with any other kind of detector, one seeks full coverage of the detection space with the anomaly detector being used. Even if coverage of a particular anomaly detector falls short of 100%, detectors can be composed to effect broader coverage, once their respective sweet spots and blind regions are known. This paper provides a framework and a fault-injection methodology for mapping an anomaly detector's effective operating space and shows that two detectors, each designed to detect the same phenomenon, may not perform similarly, even when the event to be detected is unequivocally anomalous and should be detected by either detector. Both synthetic and real-world data are used.