RDTIDS: Rules and Decision Tree-Based Intrusion Detection System for Internet-of-Things Networks

被引：144

作者：

Ferrag, Mohamed Amine ^{[1
]}

Maglaras, Leandros ^{[2
]}

Ahmim, Ahmed ^{[3
]}

Derdour, Makhlouf ^{[4
]}

Janicke, Helge ^{[5
]}

机构：

[1] Guelma Univ, Dept Comp Sci, Guelma 24000, Algeria

[2] De Montfort Univ, Sch Comp Sci & Informat, Leicester LE1 9BH, Leics, England

[3] Mohamed Cherif Messaadia Univ, Dept Math & Comp Sci, Souk Ahras 41000, Algeria

[4] Univ Larbi Tebessi, Dept Math & Comp Sci, Tebessa 12002, Algeria

[5] Edith Cowan Univ, Cyber Secur Cooperat Res Ctr, Perth, WA 6027, Australia

来源：

FUTURE INTERNET | 2020年 / 12卷 / 03期

关键词：

intrusion detection; IDS; hybrid IDS; learning machine; hierarchical; network security; DETECTION FRAMEWORK; CYBER SECURITY; ENSEMBLE;

D O I：

10.3390/fi12030044

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 proposes a novel intrusion detection system (IDS), named RDTIDS, for Internet-of-Things (IoT) networks. The RDTIDS combines different classifier approaches which are based on decision tree and rules-based concepts, namely, REP Tree, JRip algorithm and Forest PA. Specifically, the first and second method take as inputs features of the data set, and classify the network traffic as Attack/Benign. The third classifier uses features of the initial data set in addition to the outputs of the first and the second classifier as inputs. The experimental results obtained by analyzing the proposed IDS using the CICIDS2017 dataset and BoT-IoT dataset, attest their superiority in terms of accuracy, detection rate, false alarm rate and time overhead as compared to state of the art existing schemes.

引用

页数：14

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