eDoS Mitigation for Autonomic Management on Multi-Tier IoT

In this age of the Internet of Things and ubiquitous computing, autonomic management has become a critical component in cloud platforms. Autonomic management helps systems adapt seamlessly and efficiently to rapidly fluctuating workloads. However, economic Denial of Sustainability (eDoS) attacks can directly target the autonomic management to waste resources. In this paper, we propose an eDoS mitigation framework that incorporates online anomaly detection with our Elascale autonomic management system to thwart eDoS attacks in real-time. This allows the detection system to be application-agnostic as this framework utilizes only resource statistics of the monitoring applications. We present the design and implementation of our anomaly detection framework with Elascale. We evaluate Hierarchical Temporal Memory (HTM) and Tukey with Relative Entropy against spatial and temporal anomalies. Our results prove that the HTM-based anomaly detection method outperforms with significant accuracy.