| Title | Application of Machine Learning in Network Security Situational Awareness |
| Publication Type | Conference Paper |
| Year of Publication | 2021 |
| Authors | Yifan, Zhao |
| Conference Name | 2021 World Conference on Computing and Communication Technologies (WCCCT) |
| Keywords | composability, Education, feature extraction, machine learning, machine learning algorithms, Network security, Prediction algorithms, Predictive Metrics, privacy, pubcrawl, resilience, Resiliency, security, situational awareness, Support vector machines |
| Abstract | Along with the advance of science and technology, informationization society construction is gradually perfect. The development of modern information technology has driven the growth of the entire network spatial data, and network security is a matter of national security. There are several countries included in the national security strategy, with the increase of network space connected point, traditional network security space processing way already cannot adapt to the demand. Machine learning can effectively solve the problem of network security. Around the machine learning technology applied in the field of network security research results, this paper introduces the basic concept of network security situational awareness system, the basic model, and system framework. Based on machine learning, this paper elaborates the network security situation awareness technology, including data mining technology, feature extraction technology and situation prediction technology. Recursive feature elimination, decision tree algorithm, support vector machine, and future research direction in the field of network security situational awareness are also discussed. |
| DOI | 10.1109/WCCCT52091.2021.00015 |
| Citation Key | yifan_application_2021 |
Application of Machine Learning in Network Security Situational Awareness