Anomaly Detection System Using Beta Mixture Models and Outlier Detection

被引：30

作者：

Moustafa, Nour ^{[1
]}

Creech, Gideon ^{[1
]}

Slay, Jill ^{[1
]}

机构：

[1] Univ New South Wales, Australian Ctr Cyber Secur, Canberra, ACT, Australia

来源：

PROGRESS IN COMPUTING, ANALYTICS AND NETWORKING, ICCAN 2017 | 2018年 / 710卷

关键词：

Intrusion detection system (IDS); Anomaly detection system (ADS); Beta mixture model (BMM); Outlier detection;

D O I：

10.1007/978-981-10-7871-2_13

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

An intrusion detection system (IDS) plays a significant role in recognising suspicious activities in hosts or networks, even though this system still has the challenge of producing high false positive rates with the degradation of its performance. This paper suggests a new beta mixture technique (BMM-ADS) using the principle of anomaly detection. This establishes a profile from the normal data and considers any deviation from this profile as an anomaly. The experimental outcomes show that the BMM-ADS technique provides a higher detection rate and lower false rate than three recent techniques on the UNSW-NB15 data set.

引用

页码：125 / 135

页数：11

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