Intelligent Traffic Monitoring for Engineering Monitoring in Measurement Information Network

This paper discusses intelligent decision-making traffic monitoring and identification systems in the field of engineering monitoring and measurement network security, focusing on the importance of network traffic anomaly detection. The field of engineering monitoring and metrology plays a key role in all areas of engineering, ensuring the stability, reliability and efficiency of engineering systems. Cybersecurity has a place to be reckoned with in this area, as it not only protects data integrity and confidentiality, but also protects against cyberattacks, maintains system availability, ensures compliance, and ultimately helps ensure the safe operation of engineering systems. This paper also introduces important advances in deep learning models in network traffic anomaly detection, such as convolutional neural networks and recurrent neural networks, as well as the application of feature learning and transfer learning. The paper then elaborates on the principles and design of logistic regression models, emphasizing their advantages in the field of engineering monitoring, including efficiency and interpretability. Next, the key steps of system design are outlined, including requirements analysis, data acquisition and preprocessing, model design, system architecture design, user interface design, and performance optimization. Security considerations in the system design are also emphasized to ensure data protection and system stability. Finally, this paper presents challenges to consider in model training, such as overfitting and underfitting, and how to select appropriate epoch values. By analyzing the experimental results, we conclude that the choice of epoch values should strike a balance between training efficiency and performance, usually between 20–40 as the best.