LDoS attack detection method based on simple statistical features

被引：0

作者：

Duan X. ^{[1
,2
,3
]}

Fu Y. ^{[1
]}

Wang K. ^{[1
,4
]}

Li B. ^{[1
]}

机构：

[1] Department of Information Security, Naval University of Engineering, Wuhan

[2] College of Computer and Information Technology, Xinyang Normal University, Xinyang

[3] Henan Key Laboratory of Analysis and Applications of Education Big Data, Xinyang Normal University, Xinyang

[4] School of Mathematics and Information Engineering, Xinyang Vocational and Technical College, Xinyang

来源：

Tongxin Xuebao/Journal on Communications | 2022年 / 43卷 / 11期

基金：

国家重点研发计划;

关键词：

attack detection; deep learning; low-rate denial of service; statistical features;

D O I：

10.11959/j.issn.1000-436x.2022216

中图分类号：

学科分类号：

摘要：

Traditional low-rate denial of service (LDoS) attack detection methods were complex in feature extraction, high in computational cost, single in experimental data background settings, and outdated in attack scenarios, so it was difficult to meet the demand for LDoS attack detection in a real network environment. By studying the principle of LDoS attack and analyzing the features of LDoS attack traffic, a detection method of LDoS attack based on simple statistical features of network traffic was proposed. By using the simple statistical features of network traffic packets, the detection data sequence was constructed, the time correlation features of input samples were extracted by deep learning technology, and the LDoS attack judgment was made according to the difference between the reconstructed sequence and the original input sequence. Experimental results show that the proposed method can effectively detect the LDoS attack traffic in traffic and has strong adaptability to heterogeneous network traffic. © 2022 Editorial Board of Journal on Communications. All rights reserved.

引用

页码：53 / 64

页数：11

共 29 条

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