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Managing Trust and Detecting Malicious Groups in Peer-to-Peer IoT Networks
Name
sensors-21-04484-v2.pdf
Size
15.43 MB
Format
Adobe PDF
Checksum (MD5)
0a1468771508f156565c943100486a1e
Author(s)
Alhussain, Alanoud
Kurdi, Heba
Altoaimy, Lina
Date Issued
June 30, 2021
Publisher
Multidisciplinary Digital Publishing Institute
Citation
Sensors 21 (13): 4484 (2021)
Version
Final published version
Abstract
Peer-to-peer (P2P) networking is becoming prevalent in Internet of Thing (IoT) platforms due to its low-cost low-latency advantages over cloud-based solutions. However, P2P networking suffers from several critical security flaws that expose devices to remote attacks, eavesdropping and credential theft due to malicious peers who actively work to compromise networks. Therefore, trust and reputation management systems are emerging to address this problem. However, most systems struggle to identify new smart models of malicious peers, especially those who cooperate together to harm other peers. This paper proposes an intelligent trust management system, namely, Trutect, to tackle this issue. Trutect exploits the power of neural networks to provide recommendations on the trustworthiness of each peer. The system identifies the specific model of an individual peer, whether good or malicious. The system also detects malicious collectives and their suspicious group members. The experimental results show that compared to rival trust management systems, Trutect raises the success rates of good peers at a significantly lower running time. It is also capable of accurately identifying the peer model.
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Creative Commons Attribution
Persistent DSpace Link
DOI of Published Version
http://dx.doi.org/10.3390/s21134484
