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. 2022 Jun 16;22(12):4539.
doi: 10.3390/s22124539.

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

Umer Farooq  1 , Muhammad Asim  1 , Noshina Tariq  2 , Thar Baker  3 , Ali Ismail Awad  4   5   6   7
Affiliations

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

Umer Farooq et al. Sensors (Basel). .

Abstract

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.

Keywords: Internet of Things; RPL; Sybil attack; mobile agent; rank attack; sinkhole attack; trust.

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Conflict of interest statement

The authors declare that there is no conflict of interest.

Figures

Figure 1
Figure 1
Proposed device and control layers.
Figure 2
Figure 2
Topology discovery in RPL.
Figure 3
Figure 3
Workflow of the proposed MMTM-RPL framework. The numbers in the figure represent the steps taken for malicious node detection and isolation.
Figure 4
Figure 4
Comparison of network lifetime for different attacks: (a) Rank attack, (b) Sybil attack, and (c) Sinkhole attack.
Figure 4
Figure 4
Comparison of network lifetime for different attacks: (a) Rank attack, (b) Sybil attack, and (c) Sinkhole attack.
Figure 5
Figure 5
Comparison of average residual energy for different attacks: (a) Rank attack, (b) Sybil attack, and (c) Sinkhole attack.
Figure 5
Figure 5
Comparison of average residual energy for different attacks: (a) Rank attack, (b) Sybil attack, and (c) Sinkhole attack.
Figure 6
Figure 6
Comparison of control message overhead for different attacks: (a) Rank attack, (b) Sybil attack, and (c) Sinkhole attack.
Figure 6
Figure 6
Comparison of control message overhead for different attacks: (a) Rank attack, (b) Sybil attack, and (c) Sinkhole attack.
Figure 7
Figure 7
Comparison of attack detection rate for different attacks: (a) Rank attack, (b) Sybil attack, and (c) Sinkhole attack.
Figure 8
Figure 8
Comparison of attack detection time for different attacks: (a) Rank attack, (b) Sybil attack, and (c) Sinkhole attack.
Figure 9
Figure 9
Comparison of end-to-end delay for different attacks: (a) Rank attack, (b) Sybil attack, and (c) Sinkhole attack.
Figure 10
Figure 10
Overview of results for the different frameworks: (a) Network lifetime, (b) Average residual energy, (c) Control message overhead, (d) Attack detection rate, (e) Attack detection time, (f) End-to-end delay.
See this image and copyright information in PMC

References

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