Network Anomaly Detection Integrating Dynamic Graph Embedding and Transformer Autoencoder

Network anomaly detection aims to promptly identify and respond to malicious activities and potential threats within networks. Most existing graph-embedding-based methods are designed for static graphs and neglect fine-grained temporal information, thus failing to capture the continuity of dynamic n...

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Published in:Ji suan ji gong cheng Vol. 51; no. 4; pp. 47 - 56
Main Author: ZHANG Anqin, DING Zhifeng
Format: Journal Article
Language:Chinese
English
Published: Editorial Office of Computer Engineering 15.04.2025
Subjects:
dynamic graph embedding|transformer autoencoder|network anomaly detection|malicious behavior|long and short-term time-dependence
ISSN:1000-3428
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Abstract Network anomaly detection aims to promptly identify and respond to malicious activities and potential threats within networks. Most existing graph-embedding-based methods are designed for static graphs and neglect fine-grained temporal information, thus failing to capture the continuity of dynamic network behaviors and diminishing the effectiveness of network anomaly detection. To enhance the efficiency and accuracy of dynamic network anomaly detection, this study proposes a novel method integrating dynamic graph embedding and Transformer autoencoders. This method leverages temporal-walk-based graph embedding to capture the topological structure and detailed temporal information of the network. It incorporates a Transformer autoencoder with contrastive loss to optimize node embeddings and effectively capture long-term dependencies and global information. This integration enhances the model's ability to perceive dynamic networks, facilitating better detection of time-evolving events and the identification of malicious behaviors. The effectiveness of this method is validated through extensive experiments conducted on two publicly available datasets in network security. Its superior performance on the LANL-2015 dataset is indicated with a True Positive Rate (TPR) of 94.3%, False Positive Rate (FPR) of 5.7%, and an Area Under the Curve (AUC) of 98.3%. Further, on the OpTC dataset, the method achieves a TPR of 99.9%, a FPR of 0.01%, and an AUC of 99.9%. These results demonstrate that the proposed method effectively learns the topology and temporal dependencies of dynamic networks, thereby accurately identifying network anomalies.
AbstractList Network anomaly detection aims to promptly identify and respond to malicious activities and potential threats within networks. Most existing graph-embedding-based methods are designed for static graphs and neglect fine-grained temporal information, thus failing to capture the continuity of dynamic network behaviors and diminishing the effectiveness of network anomaly detection. To enhance the efficiency and accuracy of dynamic network anomaly detection, this study proposes a novel method integrating dynamic graph embedding and Transformer autoencoders. This method leverages temporal-walk-based graph embedding to capture the topological structure and detailed temporal information of the network. It incorporates a Transformer autoencoder with contrastive loss to optimize node embeddings and effectively capture long-term dependencies and global information. This integration enhances the model's ability to perceive dynamic networks, facilitating better detection of time-evolving events and the identification of malicious behaviors. The effectiveness of this method is validated through extensive experiments conducted on two publicly available datasets in network security. Its superior performance on the LANL-2015 dataset is indicated with a True Positive Rate (TPR) of 94.3%, False Positive Rate (FPR) of 5.7%, and an Area Under the Curve (AUC) of 98.3%. Further, on the OpTC dataset, the method achieves a TPR of 99.9%, a FPR of 0.01%, and an AUC of 99.9%. These results demonstrate that the proposed method effectively learns the topology and temporal dependencies of dynamic networks, thereby accurately identifying network anomalies.
Author ZHANG Anqin, DING Zhifeng
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  organization: 1. School of Computer Science and Technology, Shanghai University of Electric Power, Shanghai 201306, China;2. Institute of Local Government Development, Shantou University, Shantou 515063, Guangdong, China
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Snippet Network anomaly detection aims to promptly identify and respond to malicious activities and potential threats within networks. Most existing...
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SubjectTerms dynamic graph embedding|transformer autoencoder|network anomaly detection|malicious behavior|long and short-term time-dependence
Title Network Anomaly Detection Integrating Dynamic Graph Embedding and Transformer Autoencoder
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