Building an Application to Monitor Abnormal Network Node Traffic using Graph Learning Method
Phan Thi Ha1, Trinh Thi Van Anh2
1Dr. Phan Thi Ha, Lecturer, Faculty of Information Technology at Posts and Telecommunications Institute of Technology (PTIT) in Ha Noi, Vietnam, and Computing Fundamental Department, FPT University, Hanoi, Viet Nam.
2Th S. Trinh Thi Van Anh, Lecturer, Faculty of Information Technology at Posts and Telecommunications Institute of Technology (PTIT) in Ha Noi, Vietnam, and Computing Fundamental Department, FPT University, Hanoi, Viet Nam.
Manuscript received on 09 July 2025 | First Revised Manuscript received on 24 July 2025 | Second Revised Manuscript received on 05 August 2025 | Manuscript Accepted on 15 August 2025 | Manuscript published on 30 August 2025 | PP: 21-25 | Volume-14 Issue-9, August 2025 | Retrieval Number: 100.1/ijitee.I112214090825 | DOI: 10.35940/ijitee.I1122.14090825
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: The authors have developed an advanced monitoring application based on state-of-the-art graph learning methods to enhance the management and operational safety of telecommunications and network systems in Vietnam. This system integrates Long Short-Term Memory (LSTM) [6], Graph Convolutional Networks (GCNs) [5], and Graph Attention Networks (GATs) [4] to model and analyse the dynamic behaviour of network nodes. By combining temporal and structural features of network data, the application is capable of detecting anomalies, forecasting potential failures, and recommending timely interventions with minimal human oversight. These predictive capabilities significantly enhance the reliability, efficiency, and safety of telecommunications infrastructure, while also reducing downtime and lowering operational and maintenance costs. Additionally, the authors have developed custom software tools to support the labelling of raw datasets collected from network nodes. These datasets originate from existing static alerting systems used by network operators. The labelling tool enables efficient and consistent annotation of data, which is crucial for training and validating machine learning models. By transforming unstructured raw logs into structured labelled data, the system ensures higher accuracy in learning algorithms. Overall, this solution offers a practical and intelligent approach to managing large-scale network systems, particularly in developing regions like Vietnam, where automation and cost efficiency are critical.
Keywords: Long Short-Term Memory (LSTM), Graph Convolutional Networks (GCNs), Graph Attention Networks (GATs).
Scope of the Article: Artificial Intelligence and Methods