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

• Published in: Sensors (Basel, Switzerland) Vol. 22; no. 12; p. 4539
• Main Authors: Farooq, Umer, Asim, Muhammad, Tariq, Noshina, Baker, Thar, Awad, Ali Ismail
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 16-06-2022; MDPI
• Subjects: Agents (artificial intelligence); Assaults; Data encryption; Design; Energy consumption; Information systems; Informationssystem; Internet of Things; mobile agent; rank attack; Routing (telecommunications); RPL; sinkhole attack; Sinkholes; Sybil attack; Traffic congestion; trust; Wireless networks; Wireless sensor networks
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s22124539

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.