Anomaly Detection in Multicore Embedded Systems

In this paper, we present an Anomaly Detection implementation with the usage of Artificial Neural Network (ANN) for Multicore Embedded Systems. The detector is built over a sophisticated Real-Time Multicore scheduling framework that allowed capturing high-quality run-time data for the Machine Learning (ML) process and provided the necessary infrastructure for the ANN to be embedded. To conceive the detector we first defined the system's sane behaviour through a set of performance counters, providing the necessary information to define an anomaly. After describing the ML process and the ANN embedding details, we evaluate the results of the detection adding a different task to the execution and showing the embedded detector was able to successfully classify over 95% of the execution, never misinterpreting an anomaly as a sane task, with no interference on application execution time, once the anomaly detector runs on core 0, which is reserved for system management and control operations. Also, the maximum delay to detect that the running task is an anomaly was equal to 1 sampling of the performance monitoring counters (configured with captures spaced by 10ms, or 100 captures per second). We conclude the experiments showing the effectiveness of our runtime ANN anomaly detector by actuating on the suspension of the tasks classified as an anomaly, maintaining a sane execution by mitigating anomalies.