Managing Trust and Detecting Malicious Groups in Peer-to-Peer IoT Networks
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 the...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-06-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/21/13/4484 |
| _version_ | 1827688456312061952 |
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| author | Alanoud Alhussain Heba Kurdi Lina Altoaimy |
| author_facet | Alanoud Alhussain Heba Kurdi Lina Altoaimy |
| author_sort | Alanoud Alhussain |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T09:56:02Z |
| format | Article |
| id | doaj.art-b17fac4702af4ba993efbc06f4b8912e |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T09:56:02Z |
| publishDate | 2021-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-b17fac4702af4ba993efbc06f4b8912e2023-11-22T02:22:51ZengMDPI AGSensors1424-82202021-06-012113448410.3390/s21134484Managing Trust and Detecting Malicious Groups in Peer-to-Peer IoT NetworksAlanoud Alhussain0Heba Kurdi1Lina Altoaimy2Computer Science Department, College of Computer and Information Sciences, King Saud University, Riyadh 11451, Saudi ArabiaComputer Science Department, College of Computer and Information Sciences, King Saud University, Riyadh 11451, Saudi ArabiaInformation Technology Department, College of Computer and Information Sciences, King Saud University, Riyadh 11451, Saudi ArabiaPeer-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.https://www.mdpi.com/1424-8220/21/13/4484IoTneural networkspeer-to-peer networksreputation managementtrust management |
| spellingShingle | Alanoud Alhussain Heba Kurdi Lina Altoaimy Managing Trust and Detecting Malicious Groups in Peer-to-Peer IoT Networks Sensors IoT neural networks peer-to-peer networks reputation management trust management |
| title | Managing Trust and Detecting Malicious Groups in Peer-to-Peer IoT Networks |
| title_full | Managing Trust and Detecting Malicious Groups in Peer-to-Peer IoT Networks |
| title_fullStr | Managing Trust and Detecting Malicious Groups in Peer-to-Peer IoT Networks |
| title_full_unstemmed | Managing Trust and Detecting Malicious Groups in Peer-to-Peer IoT Networks |
| title_short | Managing Trust and Detecting Malicious Groups in Peer-to-Peer IoT Networks |
| title_sort | managing trust and detecting malicious groups in peer to peer iot networks |
| topic | IoT neural networks peer-to-peer networks reputation management trust management |
| url | https://www.mdpi.com/1424-8220/21/13/4484 |
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