Multi-Mobile Agent Trust Framework for Mitigating Internal Attacks and Augmenting RPL Security.

Internet of Things RPL Sybil attack mobile agent rank attack sinkhole attack trust

Journal

Sensors (Basel, Switzerland)
ISSN: 1424-8220
Titre abrégé: Sensors (Basel)
Pays: Switzerland
ID NLM: 101204366

Informations de publication

Date de publication:
16 Jun 2022
Historique:
received: 14 04 2022
revised: 02 06 2022
accepted: 13 06 2022
entrez: 24 6 2022
pubmed: 25 6 2022
medline: 28 6 2022
Statut: epublish

Résumé

Recently, the Internet of Things (IoT) has emerged as an important way to connect diverse physical devices to the internet. The IoT paves the way for a slew of new cutting-edge applications. Despite the prospective benefits and many security solutions offered in the literature, the security of IoT networks remains a critical concern, considering the massive amount of data generated and transmitted. The resource-constrained, mobile, and heterogeneous nature of the IoT makes it increasingly challenging to preserve security in routing protocols, such as the routing protocol for low-power and lossy networks (RPL). RPL does not offer good protection against routing attacks, such as rank, Sybil, and sinkhole attacks. Therefore, to augment the security of RPL, this article proposes the energy-efficient multi-mobile agent-based trust framework for RPL (MMTM-RPL). The goal of MMTM-RPL is to mitigate internal attacks in IoT-based wireless sensor networks using fog layer capabilities. MMTM-RPL mitigates rank, Sybil, and sinkhole attacks while minimizing energy and message overheads by 25-30% due to the use of mobile agents and dynamic itineraries. MMTM-RPL enhances the security of RPL and improves network lifetime (by 25-30% or more) and the detection rate (by 10% or more) compared to state-of-the-art approaches, namely, DCTM-RPL, RBAM-IoT, RPL-MRC, and DSH-RPL.

Identifiants

pubmed: 35746321
pii: s22124539
doi: 10.3390/s22124539
pmc: PMC9227483
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Références

Sensors (Basel). 2019 Apr 14;19(8):
pubmed: 31013993
Sensors (Basel). 2020 Oct 16;20(20):
pubmed: 33081218
Sensors (Basel). 2020 Oct 20;20(20):
pubmed: 33092224
Sensors (Basel). 2020 Dec 22;21(1):
pubmed: 33375153

Auteurs

Umer Farooq (U)

Department of Cyber Security, National University of Computer and Emerging Sciences, Islamabad 44000, Pakistan.

Muhammad Asim (M)

Department of Cyber Security, National University of Computer and Emerging Sciences, Islamabad 44000, Pakistan.

Noshina Tariq (N)

Department of Computer Science, Shaheed Zulfikar Ali Bhutto Institute of Science and Technology, Islamabad 44000, Pakistan.

Thar Baker (T)

Department of Computer Science, College of Computing and Informatics, University of Sharjah, Sharjah P.O. Box 27272, United Arab Emirates.

Ali Ismail Awad (AI)

College of Information Technology, United Arab Emirates University, Al Ain P.O. Box 17551, United Arab Emirates.
Department of Computer Science, Electrical and Space Engineering, Luleå University of Technology, 97187 Luleå, Sweden.
Faculty of Engineering, Al-Azhar University, Qena P.O. Box 83513, Egypt.
Centre for Security, Communications and Network Research, University of Plymouth, Plymouth PL4 8AA, UK.

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Classifications MeSH