Adversarial Impact on Anomaly Detection in Cloud Datacenters

Abstract

Cloud datacenters are engineered to meet the requirements of generalised and specialised workloads including mission-critical applications that not only generate tremendous amounts of data traces but also opens opportunities for attackers. The increasing volume and rapid changing behaviour of metric streams (e.g., CPU, network, latency, memory) in the cloud datacenters create difficulties to ensure high availability, security, and performance to cloud service providers. Several anomaly detection techniques have been developed to combat system anomalies in cloud datacenters. By injecting a fraction of well-crafted malicious samples in cloud datacenter traces, attackers can subvert the learning process and results in unacceptable false alarms. These security issues cause threats to all categories of anomaly detection. Hence, it is crucial to assess these techniques against adversaries to improve scalability and robustness. We propose a linear regression-based optimisation framework with the ability to poison data in the training phase and demonstrate its effectiveness on cloud datacenter traces. Finally, we investigate the worst-case analysis of poisoning attacks on robust statistics-based anomaly detection techniques to quantify and assess the detection accuracy. We validate this framework using benchmark resource traces obtained from Yahoo s service cluster as well as traces collected from an experimental testbed with realistic service composition.