DCONST: Detection of Multiple-Mix-Attack Malicious Nodes Using Consensus-Based Trust in IoT Networks

Zuchao Ma; Liang Liu; Weizhi Meng

doi:10.1007/978-3-030-55304-3_13

DCONST: Detection of Multiple-Mix-Attack Malicious Nodes Using Consensus-Based Trust in IoT Networks

Zuchao Ma, Liang Liu, Weizhi Meng^*

^*Corresponding author for this work

Department of Applied Mathematics and Computer Science

Nanjing University of Aeronautics and Astronautics

Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

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Abstract

The Internet of Things (IoT) is growing rapidly, which allows many smart devices to connect and cooperate with each other. While for the sake of distributed architecture, an IoT environment is known to be vulnerable to insider attacks. In this work, we focus on this challenge and consider an advanced insider threat, called multiple-mix attack, which typically combines three sub-attacks: tamper attack, drop attack and replay attack. For protection, we develop a Distributed Consensus based Trust Model (DCONST), which can build the nodes’ reputation by sharing particular information, called cognition. In particular, DCONST can detect malicious nodes by using the K-Means clustering, without disturbing the normal operations of a network. In the evaluation, as compared with some similar models, DCONST can overall provide a better detection rate by increasing around 10% to 40%.

Original language	English
Title of host publication	Proceedings of 25^th Australasian Conference on Information Security and Privacy
Editors	Joseph K. Liu, Hui Cui
Publisher	Springer
Publication date	2020
Pages	247-267
ISBN (Print)	9783030553036
DOIs	https://doi.org/10.1007/978-3-030-55304-3_13
Publication status	Published - 2020
Event	25th Australasian Conference on Information Security and Privacy - Perth, Australia Duration: 30 Nov 2020 → 2 Dec 2020 Conference number: 25

Conference

Conference	25th Australasian Conference on Information Security and Privacy
Number	25
Country/Territory	Australia
City	Perth
Period	30/11/2020 → 02/12/2020

Series	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	12248 LNCS
ISSN	0302-9743

Keywords

Consensus
IoT network
K-means method
Malicious node
Trust management

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Ma, Zuchao ; Liu, Liang ; Meng, Weizhi. / DCONST : Detection of Multiple-Mix-Attack Malicious Nodes Using Consensus-Based Trust in IoT Networks. Proceedings of 25^th Australasian Conference on Information Security and Privacy. editor / Joseph K. Liu ; Hui Cui. Springer, 2020. pp. 247-267 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol. 12248 LNCS).

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abstract = "The Internet of Things (IoT) is growing rapidly, which allows many smart devices to connect and cooperate with each other. While for the sake of distributed architecture, an IoT environment is known to be vulnerable to insider attacks. In this work, we focus on this challenge and consider an advanced insider threat, called multiple-mix attack, which typically combines three sub-attacks: tamper attack, drop attack and replay attack. For protection, we develop a Distributed Consensus based Trust Model (DCONST), which can build the nodes{\textquoteright} reputation by sharing particular information, called cognition. In particular, DCONST can detect malicious nodes by using the K-Means clustering, without disturbing the normal operations of a network. In the evaluation, as compared with some similar models, DCONST can overall provide a better detection rate by increasing around 10% to 40%.",

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Ma, Z, Liu, L & Meng, W 2020, DCONST: Detection of Multiple-Mix-Attack Malicious Nodes Using Consensus-Based Trust in IoT Networks. in JK Liu & H Cui (eds), Proceedings of 25^th Australasian Conference on Information Security and Privacy. Springer, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 12248 LNCS, pp. 247-267, 25th Australasian Conference on Information Security and Privacy, Perth, Western Australia, Australia, 30/11/2020. https://doi.org/10.1007/978-3-030-55304-3_13

DCONST: Detection of Multiple-Mix-Attack Malicious Nodes Using Consensus-Based Trust in IoT Networks. / Ma, Zuchao; Liu, Liang; Meng, Weizhi.
Proceedings of 25^th Australasian Conference on Information Security and Privacy. ed. / Joseph K. Liu; Hui Cui. Springer, 2020. p. 247-267 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol. 12248 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

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Ma Z, Liu L, Meng W. DCONST: Detection of Multiple-Mix-Attack Malicious Nodes Using Consensus-Based Trust in IoT Networks. In Liu JK, Cui H, editors, Proceedings of 25^th Australasian Conference on Information Security and Privacy. Springer. 2020. p. 247-267. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol. 12248 LNCS). doi: 10.1007/978-3-030-55304-3_13

DCONST: Detection of Multiple-Mix-Attack Malicious Nodes Using Consensus-Based Trust in IoT Networks

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