Attack-Detection Architectural Framework Based on Anomalous Patterns of System Performance and Resource Utilization—Part II

This paper presents a unique security approach for detecting cyber-attacks against embedded systems (ESs). The proposed has been shaped within an architectural framework called anomalous resource consumption detection (ARCD). approach’s mechanism detects by distinguishing performance and patterns from pre-determinable reference model. defense of this acts as additional layer protection ESs. technique’s effectiveness was previously evaluated statistically, in paper, we tested efficiency computationally using the support-vector machine algorithm. datasets were generated collected based on testbed model, where it run repeatedly under different operation conditions (normal cases (Rs) versus attacked cases). executed attack scenarios are 1) denial-of-service (DoS); 2) brute force (BF); 3) remote code execution (RCE), man-in-the-middle (MITM). A septenary tuple which consists seven determinants that analyzed statistical criteria, is core mechanism. prediction accuracy terms classifying compared to normal confusion matrix revealed promising results, proving effectiveness, final results confirmed very high accuracies typical patterns. Integrating ARCD concept into operating system’s functionality could help software developers augment existing countermeasures Adopting will pave way engineers build more secure line with capabilities, without depleting its resources.