Isolation-based Spherical Ensemble Representations for Anomaly Detection

2025-10-15

AI Summary

Unsupervised anomaly detection faces three key challenges: conflicting distributional assumptions, low computational efficiency, and difficulty in unifying diverse anomaly types. To address these, we propose ISER—a novel framework that encodes local density via hypersphere radius and establishes a similarity-based anomaly scoring mechanism. ISER introduces a theoretically grounded anomaly reference pattern to ensure consistency in pattern evaluation and refines the Isolation Forest scoring function to mitigate axis-aligned bias and improve detection of local anomalies. Crucially, ISER achieves efficient and robust detection while maintaining linear time and constant space complexity. Extensive experiments across 22 real-world datasets demonstrate that ISER significantly outperforms 11 state-of-the-art baseline methods, validating its effectiveness, generality, and scalability.

Technology Category

• Data Mining & Knowledge Management: Anomaly/Outlier Detection
• Machine Learning: Unsupervised & Self-Supervised Learning
• Search and Optimization: Local Search

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods

Abstract

Anomaly detection is a critical task in data mining and management with applications spanning fraud detection, network security, and log monitoring. Despite extensive research, existing unsupervised anomaly detection methods still face fundamental challenges including conflicting distributional assumptions, computational inefficiency, and difficulty handling different anomaly types. To address these problems, we propose ISER (Isolation-based Spherical Ensemble Representations) that extends existing isolation-based methods by using hypersphere radii as proxies for local density characteristics while maintaining linear time and constant space complexity. ISER constructs ensemble representations where hypersphere radii encode density information: smaller radii indicate dense regions while larger radii correspond to sparse areas. We introduce a novel similarity-based scoring method that measures pattern consistency by comparing ensemble representations against a theoretical anomaly reference pattern. Additionally, we enhance the performance of Isolation Forest by using ISER and adapting the scoring function to address axis-parallel bias and local anomaly detection limitations. Comprehensive experiments on 22 real-world datasets demonstrate ISER's superior performance over 11 baseline methods.

Problem

• Addresses conflicting distributional assumptions in unsupervised anomaly detection
• Improves computational efficiency while maintaining linear time complexity
• Enhances detection of different anomaly types using ensemble representations

Innovation

• Hypersphere radii encode local density characteristics
• Similarity scoring compares patterns against anomaly reference
• Enhances Isolation Forest by addressing bias limitations