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Cybersecurity attacks in Cloud data centres are increasing alongside thegrowth of the Cloud services market. Existing research proposes a number ofanomaly detection systems for detecting such attacks. However, these systemsencounter a number of challenges, specifically due to the unknown behaviour ofthe attacks and the occurrence of genuine Cloud workload spikes, which must bedistinguished from attacks. In this paper, we discuss these challenges andinvestigate the issues with the existing Cloud anomaly detection approaches.Then, we propose a Real-time Anomaly Detection System (RADS) for Cloud datacentres, which uses a one class classification algorithm and a window-basedtime series analysis to address the challenges. Specifically, RADS can detectVM-level anomalies occurring due to DDoS and cryptomining attacks. We evaluatethe performance of RADS by running lab-based experiments and by usingreal-world Cloud workload traces. Evaluation results demonstrate that RADS canachieve 90-95% accuracy with a low false positive rate of 0-3%. The resultsfurther reveal that RADS experiences fewer false positives when using itswindow-based time series analysis in comparison to using state-of-the-artaverage or entropy based analysis.

RADS: Real-time Anomaly Detection System for Cloud Data Centres