Network Data Unsupervised Clustering to Anomaly Detection

Proceedings

Dafonte, Carlos¹  López-Vizcaíno, Manuel²  Nóvoa, Francisco J.³ 

[1] Author to whom correspondence should be addressed.;CITIC, UDC, Campus de Elviña s/n, 15071 A Coruña, Spain;Presented at the XoveTIC Congress, A Coruña, Spain, 27--28 September 2018

关键词: Self-Organizing Maps;    IDS;    network security;    categorical SOM;    visualization;    unsupervised clustering;   

DOI  :  10.3390/proceedings2181173

学科分类：社会科学、人文和艺术（综合）

来源: mdpi

【 摘 要 】

In these days, organizations rely on the availability and security of their communication networks to perform daily operations. As a result, network data must be analyzed in order to provide an adequate level of security and to detect anomalies or malfunctions in the systems. Due to the increase of devices connected to these networks, the complexity to analyze data related to its communications also grows. We propose a method, based on Self-Organized Maps, which combine numerical and categorical features, to ease communication network data analysis. Also, we have explored the possibility of using different sources of data.

【 授权许可】

CC BY