Insider Threat Detection Using Principal Component Analysis and Self-Organising Map

被引：2

作者：

Moradpoor, Naghmeh ^{[1
]}

Brown, Martyn ^{[1
]}

Russell, Gordon ^{[1
]}

机构：

[1] Edinburgh Napier Univ, Sch Comp, Edinburgh, Midlothian, Scotland

来源：

SIN'17: PROCEEDINGS OF THE 10TH INTERNATIONAL CONFERENCE ON SECURITY OF INFORMATION AND NETWORKS | 2017年

关键词：

Insider Threat; Unsupervised Machine Learning; Self-Organising Map; Principal Component Analysis;

D O I：

10.1145/3136825.3136859

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

An insider threat can take on many aspects. Some employees abuse their positions of trust by disrupting normal operations, while others export valuable or confidential data which can damage the employer's marketing position and reputation. In addition, some just lose their credentials which are then abused in their name. In this paper, we use Principal Component Analysis (PCA) in conjunction with Self-Organising Map (SOM) for insider threat detection within an organisation. The results show that using PCA before SOM increases the clustering accuracy.

引用

页码：274 / 279

页数：6

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