Summary:
Network traffic analysis is an important research direction in security threat detection. Current traffic analysis mainly adopts the method of matching event characteristic information with the signature database. However, this method has the disadvantages of simple organization of the signature database and untimely updates. In addition, persistent attack technology updates quickly and can easily circumvent existing rules, resulting in a high rate of false negatives and false positives in detection. For this purpose, a network traffic security intelligent analysis system is proposed and designed. The system can automatically learn the characteristics of network traffic, intelligently identify anomalies and conduct in-depth analysis, so that it can accurately and quickly locate problems and improve security protection capabilities.
Content directory:
1 Application of AI in the security field
2 Current status of AI applications in the security field
3 Application of AI in intelligent analysis of network traffic
3.1 System functional architecture
3.2 System software architecture
3.3 Key technologies of the system
3.3.1 Network threat signature profile scanning technology based on self-learning
3.3.2 Covert malicious traffic detection technology
3.3.3 Behavior sequence modeling technology based on hidden Markov
3.3.4 Abnormal login behavior detection technology
3.3.5 Malicious link detection technology
3.3.6 Distributed correlation analysis technology based on big data
4 System implementation, demonstration and verification
4.1 Demonstration and verification environment
4.2 System analysis model
4.3 Key indicator verification
5 application scenarios
6 Conclusion
With the rapid development of Internet technology and applications, network traffic has experienced explosive growth. In terms of security, blurred network boundaries bring certain challenges to traffic monitoring, and the increase in malicious traffic increases the difficulty of security protection. In recent years, artificial intelligence (AI) technology with machine learning as the core has achieved good application results in computer vision, speech recognition, natural language processing, etc., demonstrating the role of machine learning in classification, prediction, and assisted decision-making. capability advantages, and also provide solutions for