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Anomaly Detection in Cloud Computing Workloads Based on Resource Usage
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DOI：10.1007/s10922-025-09967-4.png)
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Abstract
En 中文
Cloud computing services face increasing security threats, which is a challenging problem. The existing anomaly detection methods struggle with multi-metric correlations and missing data. To address these challenges, this paper proposes Pattern-AD, a novel anomaly detection method that models attacks as anomalies against the system’s normal states. Unlike traditional approaches, Pattern-AD extracts frequent patterns using the Apriori data mining algorithm, offering flexibility regardless of pattern length. Evaluated on the GWA-T-12 dataset (1750 VMs), the proposed Pattern-AD achieves 99.98% accuracy, outperforming KNN and Isolation Forest by $$\ge$$ 49%, with an event processing latency of 1.5 s. Most importantly, it maintains 96.55% accuracy even with 20% missing data, a capability unmatched by deep learning alternatives. This provides cloud operators with an interpretable and lightweight solution for anomaly detection.
Keywords:
Cloud computing services
Anomaly detection
Time series
Pattern extraction
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