Application of Network Anomaly Detection Algorithm Based on Deep Learning in Information Communication

With more and more electronic devices connected to the Internet, the scale of the network continues to expand. How to ensure the security and stability of the network becomes critical. Network anomaly detection technology, as an important part of network security, is playing an increasingly important role. Network anomaly detection technology, by establishing benchmarks for normal network behavior, can accurately identify abnormal behaviors that deviate from these benchmarks, thereby detecting new types of network attacks and potential security threats. This technology has strong universality and can be applied not only to intrusion detection, but also to various fields such as botnet detection and malicious traffic identification. In this article, we propose a network anomaly detection algorithm based on deep learning (DL). DL, as a powerful machine learning (ML) method, can automatically extract complex feature patterns from massive network data, providing strong support for network anomaly detection. The experimental results show that the algorithm has significant application effects in the field of information and communication. It not only has high detection accuracy, but also can quickly respond to various network abnormal events, providing a solid guarantee for network security.