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<p>This paper introduces the Adaptive Local Outlier Factor (AlynLOF), a novel streaming anomaly detection algorithm that integrates the Granularity k-Nearest Neighbor (GkNN) method. Unlike traditional LOF and recent variants like EiLOF, which depend on fixed, user-defined parameters, GkNN automatically computes a single global optimal <i>k</i> by analyzing the granularity structure of the current data window. This allows the algorithm to adapt autonomously to evolving data distributions. A key advantage of AlynLOF is its constant-memory design; it retains a fixed buffer of only 100 relevant data points, using a Kneedle algorithm and weight mechanism to discard outdated or anomalous instances, thereby ensuring scalability. Extensive empirical validation across 17 benchmark datasets compares AlynLOF against 9 state-of-the-art algorithms. The results demonstrate that AlynLOF achieves the highest average ROC AUC of 0.7780 and the best average rank based on Skillings-Mack which is 9.6, significantly outperforming competitors such as LODA (rank 6.2) and EiLOF (rank 6.0). Pairwise statistical tests using Wilcoxon and Holm correction confirm that AlynLOF achieves superior accuracy (<InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(p&lt;0.05\)</EquationSource> </InlineEquation>) against eight baseline methods, establishing it as a robust solution for real-time anomaly detection in memory-constrained environments.</p>

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Adaptive local outlier factor

  • 期刊论文

  • 发表日期：

    2026年3月14日

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  • Muhammad Yunus Iqbal Basheer,
  • Azliza Mohd Ali,
  • Nurzeatul Hamimah Abdul Hamid,
  • Sharifalillah Nordin,
  • Rozianawaty Osman,
  • Nooraini Yusoff,
  • Xiaowei Gu

摘要

This paper introduces the Adaptive Local Outlier Factor (AlynLOF), a novel streaming anomaly detection algorithm that integrates the Granularity k-Nearest Neighbor (GkNN) method. Unlike traditional LOF and recent variants like EiLOF, which depend on fixed, user-defined parameters, GkNN automatically computes a single global optimal k by analyzing the granularity structure of the current data window. This allows the algorithm to adapt autonomously to evolving data distributions. A key advantage of AlynLOF is its constant-memory design; it retains a fixed buffer of only 100 relevant data points, using a Kneedle algorithm and weight mechanism to discard outdated or anomalous instances, thereby ensuring scalability. Extensive empirical validation across 17 benchmark datasets compares AlynLOF against 9 state-of-the-art algorithms. The results demonstrate that AlynLOF achieves the highest average ROC AUC of 0.7780 and the best average rank based on Skillings-Mack which is 9.6, significantly outperforming competitors such as LODA (rank 6.2) and EiLOF (rank 6.0). Pairwise statistical tests using Wilcoxon and Holm correction confirm that AlynLOF achieves superior accuracy ( \(p<0.05\) ) against eight baseline methods, establishing it as a robust solution for real-time anomaly detection in memory-constrained environments.