Dimensional Reduction With Fast ICA for IoT Botnet Detection

被引：4

作者：

Susanto ^{[1
,2
]}

Stiawan, Deris ^{[3
]}

Rini, Dian Palupi ^{[3
]}

Arifin, M. Agus Syamsul ^{[1
,2
]}

Idris, Mohd Yazid ^{[4
]}

Alsharif, Nizar ^{[5
]}

Budiarto, Rahmat ^{[5
]}

机构：

[1] Univ Sriwijaya, Fac Engn, Sriwijaya, Indonesia

[2] Univ Bina Insan, Fac Comp, Lubuklinggau, Indonesia

[3] Univ Sriwijaya, Fac Comp Sci, Dept Comp Engn, Palembang, Indonesia

[4] Univ Teknol, Fac Engn, Sch Comp, Johor Baharu, Malaysia

[5] Albaha Univ, Coll Comp Sci & IT, Albaha, Saudi Arabia

来源：

JOURNAL OF APPLIED SECURITY RESEARCH | 2023年 / 18卷 / 04期

关键词：

IoT botnet; IDS; dimensional reduction; fast ICA; scalability; INDEPENDENT COMPONENT ANALYSIS; INTRUSION; CLASSIFIER; ALGORITHMS;

D O I：

10.1080/19361610.2022.2079906

中图分类号：

DF [法律]; D9 [法律];

学科分类号：

0301 ;

摘要：

The Internet of Things (IoT) has unique characteristics with a minimalist design and has network access with great scalability, which makes it difficult to control access. Setting up an intrusion detection system (IDS) on an IoT system while taking into account its unique characteristics is a big challenge. In this paper, we propose a dimensional reduction approach utilizing the fast independent component analysis (ICA) method to address scalability issues of IDS for IoT systems. Experimental results show that the reduction of dimensions by the fast ICA method overall improves the IDS execution time and does not significantly affect accuracy.

引用

页码：665 / 688

页数：24

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