Improving Database Anomaly Detection Efficiency through Sample Difficulty Estimation

This paper presents a novel approach to improving database anomaly detection efficiency through sample difficulty estimation. Traditional anomaly detection methods often apply uniform computational resources across all data samples regardless of their complexity, resulting in inefficient resource utilization. Our framework addresses this limitation by quantifying the "difficulty" of individual database instances and strategically allocating computational resources where they provide maximum benefit. The proposed model combines isolation scores, density-based metrics, and surprise adequacy measurements to comprehensively assess sample difficulty. Based on these assessments, a difficulty-oriented priority assignment mechanism implemented through a sigmoid mapping function directs intensive computational efforts to challenging cases while processing simpler samples with lighter methods. Experimental evaluation across five diverse datasets demonstrates that our approach achieves a 52.84% reduction in average processing time compared to uniform approaches, while maintaining or improving detection accuracy. The framework achieves the highest Average Percentage of Faults Detected (APFD) score of 0.915, outperforming both traditional and deep learning-based methods. This research provides a foundation for developing intelligent, resource-aware anomaly detection systems capable of handling the increasing scale and complexity of modern database environments.