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Research on anomaly detection method for hybrid big data subarea based on ant colony algorithm

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成果类型:
期刊论文
作者:
Shu Xu
通讯作者:
Xu, S.
作者机构:
[Shu Xu] College of Information and Electronic Engineering, Hunan City University, Yiyang, Hunan, 413000, China
通讯机构:
College of Information and Electronic Engineering, Hunan City University, Yiyang, Hunan, China
语种:
英文
关键词:
ant colony algorithm;mixed type;big data;subregion;abnormal detection;weighted network nodes;coordinate matrix
期刊:
International Journal of Information and Communication Technology
ISSN:
1466-6642
年:
2020
卷:
17
期:
2
页码:
164-177
机构署名:
本校为第一且通讯机构
院系归属:
信息与电子工程学院
摘要:
Due to the problems of low accuracy and poor degree of freedom of the existing big data anomaly detection methods, a mixed big data partition anomaly detection method based on ant colony algorithm is proposed. The number of common neighbourhood between nodes in weighted network is redefined and the mixed big data sub-region is realised. Combining the operation, vulnerability and threat of the database, the security situation value is substituted into the abnormal location part to form the coordinate matrix. The pheromone concentration of each region was calculated, and the region where the con...

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