Abnormal Detection Research Based on Outlier Mining

被引：4

作者：

Zhang, Lei ^{[1
]}

Liu, Changjiang ^{[2
]}

Chen, Yong ^{[3
]}

Lao, Shaowen ^{[4
]}

机构：

[1] Hexi Univ, Informat Technol Ctr, Zhangye 734000, Gansu, Peoples R China

[2] Troops 32153 PLA, XuanHua 075100, Hebei, Peoples R China

[3] Army Artillery Training base, XuanHua 075100, Hebei, Peoples R China

[4] Hexi Univ, Informat Technol & Media Inst, Zhangye 734000, Gansu, Peoples R China

来源：

2018 11TH INTERNATIONAL CONFERENCE ON INTELLIGENT COMPUTATION TECHNOLOGY AND AUTOMATION (ICICTA 2018) | 2018年

关键词：

Data mining; Abnormal detection; Outlier Mining;

D O I：

10.1109/ICICTA.2018.00009

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

This paper introduces the latest development of intrusion detection based on data mining, and studies the key technologies of network intrusion detection based on data mining, especially outlier mining. A anomaly detection model based on outlier mining is proposed. The model firstly preprocesses the data and sets the normal sample set, calculates the outlier score of each sample in the normal sample set, and combines it with the specified outlier value threshold. The normal behavior profile is formed, which can be used as the basis for detection.

引用

页码：5 / 7

页数：3

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