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<p>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 <InlineEquation ID="IEq1"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="10922_2025_9967_Article_IEq1.gif" Format="GIF" Height="15" Rendition="HTML" Resolution="72" Type="Linedraw" Width="19" /> </InlineMediaObject> <EquationSource Format="TEX">\(\ge\)</EquationSource> <EquationSource Format="MATHML"><math> <mo>≥</mo> </math></EquationSource> </InlineEquation> 49%, with an event processing latency of 1.5&#xa0;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. </p>

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Anomaly Detection in Cloud Computing Workloads Based on Resource Usage

  • 期刊论文

  • 发表日期：

    2025年7月5日

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  • Arezoo Jahani,
  • Paria Jourabchi Amirkhizi

摘要

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.