Intelligent Analysis and Dynamic Security of Network Traffic in Context of Big Data

The socialization and informatization of social life and enterprises have brought about explosive growth in network traffic. Enterprises and operators need to timely understand the operation status of network traffic and discover whether there are malicious traffic such as worms and DDOS in the traffic in a short period of time. This has brought unprecedented security challenges to individuals, enterprises, and countries. This article proposes an intelligent analysis and dynamic security detection framework, and introduces its principles, implementation methods, and applications in network traffic anomaly detection. A dynamic security strategy incorporating intrusion detection systems for enhanced vigilance and protection. This article proposes a dynamic security architecture design based on micro services and deep learning. Through the method proposed in this article, 100% of known malware attacks have been successfully identified and prevented, with a significant improvement in recognition rate compared to the previous 80%. This means that our system can more effectively protect users from potential threats. The accuracy of traffic anomaly detection has reached 99.9%, the page loading speed has increased by 30%, and user satisfaction has also increased to 90%. The research results will provide useful references for research and practice in related fields.