Detecting workload-based and instantiation-based economic denial of sustainability on 5G environments

This paper reviews the Economic Denial of Sustainability (EDoS) problem in emerging network scenarios. The performed research studied them in context of adaptive approaches grounded on self-organizing networks (SON) and Network Function Virtualization (NFV). In particular, two novel threats were reviewed in depth: Workload-based EDoS (W-EDoS) and Instantiation-based EDoS (I-EDoS). With the aim to contribute to their mitigation a security architecture with network-based intrusion detection capabilities is proposed. This architecture implements machine learning techniques, network behaviour prediction, adaptive thresholding methods, and productivity-based clustering for detecting entropy-based anomalies based on the observed workload (W-EDoS) or suspicious variations of the productivity observed at the virtual instances (I-EDoS). A detailed experimentation has been conducted considering different calibration parameters under different network scenarios, on which the security architecture has been assessed. The results have proven good accuracy levels, hence demonstrating the proposal effectiveness.