Adaptive Anomaly Detection for Multi-Dimensional Issue Reports

Modern cloud-native architectures demand automated incident identification systems to identify sparse yet critical attribute combinations in multidimensional issue report data. Therefore, there is a need for automated incident detection systems to perform rapid and accurate anomaly detection. Traditional search-based methods are hindered by the combinatorial explosion of attributes and stringent real-time detection requirements, resulting in inefficiency and poor scalability for anomaly identification in large-scale cloud-native systems. To address these challenges, we propose an Adaptive Anomaly Detection for Multi-Dimensional Issue Reports (AADMIR), a combinatorial optimization framework integrating dynamic operator scheduling with scoring-guided neighborhood expansion. Unlike traditional meta-heuristics, AADMIR introduces three core mechanisms specifically designed for high-dimensional issue analysis: (1) Adaptive Operator Selection: A regression-guided dynamic adjustment strategy that automatically selects optimal search operations based on real-time exploration progress, overcoming the ”static strategy trap” in conventional methods. (2) Neighborhood Expansion: A hierarchical second-order neighborhood expansion mechanism addresses the limitations of traditional first-order search by introducing second-order neighborhood. This dynamic topology extends the search scope while maintaining efficiency through probabilistic sampling, effectively balancing global exploration and local exploitation to mitigate local optima entrapment. (3) Regression-Driven Adaptive Judgment: We propose a lightweight, threshold‐based evaluation metric that combines mathematical change‐point detection with information entropy to quantify tuple benefits. By defining an energy metric and using the normalized change threshold, it robustly distinguishes true anomalies from normal fluctuations, avoids false positives inherent in purely entropy‐based methods, and delivers rapid greedy‐step decisions with minimal computational overhead. Experiments on synthetic and real-world datasets demonstrate that AADMIR achieves superior precision, recall, and F1-score compared to state-of-the-art methods, particularly excelling in high-dimensional scenarios.