A novel LDVP-based anomaly detection method for data streams

Abstract

Recently, some progress has been made in anomaly detection for data streams. However, these methods still exhibit deficiencies in effectively balancing computational efficiency and detection accuracy. In this paper, we propose a novel anomaly detection method called Dynamic Window Anomaly Detection based on Local Density of Vector dot Product (DWAD-LDVP) for data streams. DWAD-LDVP uses an adaptive dynamic sliding window mechanism with a verification model to balance the relationship between computational efficiency and detection accuracy. By converting local density values measured by the vector dot product into outlier scores, DWAD-LDVP can improve the accuracy of anomaly detection compared to SOTA methods. Furthermore, an incremental computation module is adopted to process data streams efficiently, thereby reducing unnecessary computational overhead. Simulation experiments on synthetic data and real-world datasets validate the robustness and superior performance of the proposed DWAD-LDVP method. Specifically, the experimental results indicate that the proposed DWAD-LDVP achieves competitive performance measured by accuracy, precision, recall, F1 score, and ROC AUC, confirming its effectiveness in anomaly detection tasks.

KEYWORDS:

• Anomaly detection
• Anomaly verification
• Data streams
• LDVP

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported by the National Natural Science Foundation of China [grand number: 62111540270, 62362064, 62161051], the Zhejiang Provincial Natural Science Foundation of China [grand number: LZ23F010003, LQ23F010009], Zhejiang Gongshang University “Digital+” Disciplinary Construction Management [grand number: SZJ2022B010], the Fundamental Research Funds for the Provincial Universities of Zhejiang [grand number: XRK22005], and the Yunnan Fundamental Research Projects [grand number: 202201AT070166].