The field of network traffic monitoring and anomaly detection is rapidly evolving, driven by the increasing complexity of network traffic and the need for enhanced security measures. Recent research has focused on leveraging large language models, machine learning, and deep learning techniques to improve detection accuracy and efficiency. Notably, the integration of attention mechanisms and transformer architectures has shown promising results in capturing complex patterns in network traffic. Additionally, the application of vision transformers to network flow packets has demonstrated effectiveness in detecting IoT botnet attacks. The development of novel frameworks and models, such as multidimensional interactive attention mechanisms and hybrid models combining CNN and BiLSTM, has also advanced the field. These innovations have achieved state-of-the-art performance in network intrusion detection tasks and have shown potential for scalable deployment in large-scale IoT environments. Some noteworthy papers include:

• A research paper introducing a large language model-based network traffic monitoring and anomaly detection system, which outperforms traditional methods in detection accuracy and computational efficiency.
• A study presenting ML-IoTrim, a system for detecting and mitigating non-essential IoT traffic, which demonstrates strong potential for scalable deployment in large-scale IoT environments.
• A paper proposing LLMPrism, a black-box performance diagnosis system for large language model training platforms, which achieves non-intrusive and continuous monitoring of LLM training systems.