Research on the Key Technology of Network Security Based on Machine Learning

With the continuous development of information technology, the huge number of network devices, applications, and the explosive expansion of network data have made the network environment increasingly complex, posing huge potential risks to network security. The targets of cyber attackers are no longer limited to cyber attacks on ordinary users, but they have shifted their targets to network environments with related backgrounds such as enterprises, governments, and countries. The diversification of network services has generated massive amounts of Internet data, and traditional network security technologies have been difficult to meet the current needs of network security in terms of performance and self-adaptability. Research on network security based on machine learning has achieved many results, showing strong capabilities in processing massive data, automatic learning, detection and identification, and broadening the development of ideas in the field of network security. In this paper, we combine machine learning-related technologies to improve intrusion detection performance and alarm correlation automation, and investigate key technologies such as machine learning-based network security situational awareness methods and dynamic data stream classification methods based on judgment feedback, in order to improve the detection performance, adaptive and generalization capabilities of machine learning-based network security technologies.