A Network Intrusion Detection Algorithm Based on Outlier Mining

被引：0

作者：

Ding, Tianyi ^{[1
]}

Zhang, Min ^{[1
]}

He, Dongjie ^{[2
]}

机构：

[1] Elect Engn Inst, Hefei 230037, Anhui, Peoples R China

[2] Harbin Inst Technol, Harbin 150001, Heilongjiang, Peoples R China

来源：

COMMUNICATIONS, SIGNAL PROCESSING, AND SYSTEMS | 2019年 / 463卷

关键词：

Intrusion detection; Outliers; Spectral clustering; LOF algorithm; Data mining; SYSTEMS;

D O I：

10.1007/978-981-10-6571-2_147

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

A spectral clustering and Local Outlier Factor (LOF) based outlier mining algorithm, which is aiming to solve network intrusion detection problem, is proposed in this paper. First of all, the structure of similarity matrix method in spectral clustering is used for data preprocessing to find out the smaller similarity objects. During this process, the pruning of the outliers is completed, and a set of candidate outliers is obtained. Then, we calculate the local outlier factor of each data object in this set through LOF algorithm. And the final results of detection of outliers are acquired. The experimental results show that the proposed algorithm improves the accuracy of detecting outliers and the effectiveness of network intrusion detection.

引用

页码：1229 / 1236

页数：8

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