Research on Cloud Platform Network Traffic Monitoring and Anomaly Detection System based on Large Language Models

• URL: http://arxiv.org/abs/2504.17807v1
• Date: Tue, 22 Apr 2025 07:42:07 GMT
• Title: Research on Cloud Platform Network Traffic Monitoring and Anomaly Detection System based on Large Language Models
• Authors: Ze Yang, Yihong Jin, Juntian Liu, Xinhe Xu, Yihan Zhang, Shuyang Ji,
• Abstract summary: This paper introduces a large language model (LLM)-based network traffic monitoring and anomaly detection system.<n>A pre-trained large language model analyzes and predicts the probable network traffic, and an anomaly detection layer considers temporality and context.<n>Results show that the designed model outperforms traditional methods in detection accuracy and computational efficiency.
• Score: 5.524069089627854
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The rapidly evolving cloud platforms and the escalating complexity of network traffic demand proper network traffic monitoring and anomaly detection to ensure network security and performance. This paper introduces a large language model (LLM)-based network traffic monitoring and anomaly detection system. In addition to existing models such as autoencoders and decision trees, we harness the power of large language models for processing sequence data from network traffic, which allows us a better capture of underlying complex patterns, as well as slight fluctuations in the dataset. We show for a given detection task, the need for a hybrid model that incorporates the attention mechanism of the transformer architecture into a supervised learning framework in order to achieve better accuracy. A pre-trained large language model analyzes and predicts the probable network traffic, and an anomaly detection layer that considers temporality and context is added. Moreover, we present a novel transfer learning-based methodology to enhance the model's effectiveness to quickly adapt to unknown network structures and adversarial conditions without requiring extensive labeled datasets. Actual results show that the designed model outperforms traditional methods in detection accuracy and computational efficiency, effectively identify various network anomalies such as zero-day attacks and traffic congestion pattern, and significantly reduce the false positive rate.

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