An Algorithm for Network Security Situation Assessment Based on Deep Learning

被引:2
作者
Wen, Zhicheng [1 ,2 ]
Peng, Linhua [1 ]
Wan, Weiqing [1 ]
Ou, Jing [2 ]
机构
[1] Jiangxi Univ Engn, Sch Big Data & Comp, Xinyu 338000, Jiangxi, Peoples R China
[2] Hunan Univ Technol, Coll Comp Sci, Zhuzhou 412007, Hunan, Peoples R China
关键词
LSTM; network security situation; assessment method; information fusion; deep learning;
D O I
10.1142/S0218001422520310
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Aiming at the problems that the existing assessment methods are difficult to solve, such as the low efficiency and uncertainty of network security situation assessment in complex network environment, by constructing the characteristic elements of network security big data, a typical model based on deep learning, long short-term memory (LSTM), is established to assess the network security situation in time series. The hidden relationship and change trend of network security situation are automatically mined and analyzed through the deep learning algorithm of big data, which greatly improves the prediction accuracy of security situation. Experimental analysis shows that this method has a better assessment effect on network threats, has higher learning efficiency than the traditional network situation assessment methods, and has strong representation ability in the face of network threats. It can more accurately and effectively assess the changing trend of big data security situation in the future.
引用
收藏
页数:18
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