Attribute graph node anomaly detection based on training strategy optimization

Pengfei Song; Hao Lu; Ning Cao; Shuwei Zhang

doi:10.1117/12.3038012

22 August 2024 Attribute graph node anomaly detection based on training strategy optimization

Pengfei Song, Hao Lu, Ning Cao, Shuwei Zhang

Author Affiliations +

Proceedings Volume 13228, Fifth International Conference on Computer Communication and Network Security (CCNS 2024); 132280U (2024) https://doi.org/10.1117/12.3038012
Event: Fifth International Conference on Computer Communication and Network Security (CCNS 2024), 2024, Guangzhou, China

Abstract

Attributional network anomaly detection is increasingly becoming a focal point of academic research due to its applications in critical domains like social networking and financial fraud. This task faces many challenges due to the differences in attributes between anomalous nodes and others, as well as their complex interactions. While existing shallow methods fall short in concurrently addressing both attributes and structures, leading to suboptimal performance, methods based on deep learning have made significant advances in enhancing anomaly detection. However, they still lack sufficient exploitation of anomaly information. In response to the aforementioned issue, this paper introduces an attribute graph node anomaly detection method based on training strategy optimization (ANATSO). The model constructs a secondary view through edge perturbation and samples subgraphs from each view. These subgraphs are then used to initialize networks for subgraph-node and node-node instance pairs. Additionally, a phased training process is implemented, where the outcomes from the initial phase are used to optimize the node input processing in the subsequent phase, ultimately calculating anomaly scores for each node. This research was evaluated on five benchmark datasets, and the results demonstrate that compared to traditional baseline methods, our model significantly improves the precision of anomaly detection.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Pengfei Song, Hao Lu, Ning Cao, and Shuwei Zhang "Attribute graph node anomaly detection based on training strategy optimization", Proc. SPIE 13228, Fifth International Conference on Computer Communication and Network Security (CCNS 2024), 132280U (22 August 2024); https://doi.org/10.1117/12.3038012

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KEYWORDS

Education and training

Data modeling

Performance modeling

Mathematical optimization

Social networks

Matrices

Deep learning

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