Sinkhole Attack in Multi-sink Paradigm: Detection and Performance Evaluation in RPL based IoT

Iqbal, M. Tariq; Ahmed, Afshan; Khadam, Umair

doi:10.1109/iccit-144147971.2020.9213797

Cited by 7 publications

(3 citation statements)

References 16 publications

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“…However, the proposed mechanism has a lower accuracy and detection rate than other state-of-theart systems. Iqbal et al [26] proposed a trust-based mechanism for detecting rank and sinkhole attacks in multi-sink networks. It is an energy-efficient mechanism.…”

Section: Related Workmentioning

confidence: 99%

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

Asim

Tariq

Baker

et al. 2022

Sensors

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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.

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Section: Related Workmentioning

confidence: 99%

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

Asim

Tariq

Baker

et al. 2022

Sensors

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“…This attack alters the topology of the network and decreases its efficiency. Also, a BH attack is where an attacker tries to divert all network traffic [118,119].…”

Section: Rpl Protocol-specific Attacksmentioning

confidence: 99%

A Review of Intrusion Detection Systems in RPL Routing Protocol Based on Machine Learning for Internet of Things Applications

Seyfollahi

Ghaffari

2021

Wireless Communications and Mobile Computing

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IPv6 routing protocol for low-power and lossy networks (RPL) has been developed as a routing agent in low-power and lossy networks (LLN), where nodes’ resource constraint nature is challenging. This protocol operates at the network layer and can create routing and optimally distribute routing information between nodes. RPL is a low-power, high-throughput IPv6 routing protocol that uses distance vectors. Each sensor-to-wire network router has a collection of fixed parents and a preferred parent on the path to the Destination-oriented directed acyclic graph (DODAG) graph’s root in steady-state. Each router part of the graph sends DODAG information object (DIO) control messages and specifies its rank within the graph, indicating its position within the network relative to the root. When a node receives a DIO message, it determines its network rank, which must be higher than all its parents’ rank, and then continues sending DIO messages using the trickle timer. As a result, DODAG begins at the root and eventually extends to encompass the whole network. This paper is the first review to study intrusion detection systems in the RPL protocol based on machine learning (ML) techniques to the best of our knowledge. The complexity of the new attack models identified for RPL and the efficiency of ML in intelligent and collaborative threats detection, and the issues of deploying ML in challenging LLN environments underscore the importance of research in this area. The analysis is done using research sources of “Google Scholar,” “Crossref,” “Scopus,” and “Web of Science” resources. The evaluations are assessed for studies from 2016 to 2021. The results are illustrated with tables and figures.

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“…Accordingly, the performance of these networks and their resilience against threat actors are continuously evaluated by scholars. Authors in [8][9][10][11][12] are examples of studies that include studying the impact of certain attacks (including flooding) on IoT, as well as the overhead associated with deploying mitigation for these attacks. To maximize the potential of WSNs and IoTs, and to Disclaimer/Publisher's Note: The statements, opinions, and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s).…”

Section: Introductionmentioning

confidence: 99%

Impact of Flooding Attack on Wireless Sensor Networks: Framework and Toolkit

AlGhofaili,

Albinali,

Azzedin

2023

Preprint

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IoTs and WSNs utilize their connectivity to enable solutions supporting a spectrum of industries in different and volatile environments. To effectively enhance the security and quality of service of networks, empirical research should take into account a variety of factors and be structured in a replicable manner. This will not only ensure scalability but also enable verification of conclusions, leading to more constructive outcomes. Cooja offers limited performance analysis capabilities of simulations, which are often extracted and calculated manually. In this paper, we introduce a framework and a toolkit that enable researchers to conduct structured and conclusive experiments considering different factors and metrics, experiment design to results analysis. Besides, this tool summarizes multiple network metrics and allows multiple scenarios. As proof of concept, this paper studies the flooding attacks on IoT and illustrates their impact on the network, leveraging the proposed framework and tools.

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Sinkhole Attack in Multi-sink Paradigm: Detection and Performance Evaluation in RPL based IoT

Cited by 7 publications

References 16 publications

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

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

A Review of Intrusion Detection Systems in RPL Routing Protocol Based on Machine Learning for Internet of Things Applications

Impact of Flooding Attack on Wireless Sensor Networks: Framework and Toolkit

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