Intrusion Detection Systems in RPL-Based 6LoWPAN: A Systematic Literature Review

Pasikhani, Aryan Mohammadi; Clark, John A.; Gope, Prosanta; Alshahrani, Abdulmonem

doi:10.1109/jsen.2021.3068240

Cited by 35 publications

(49 citation statements)

References 100 publications

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“…This attack is brutal to detect, owing to its static nature. When an unknown intruder is involved, the best way to avoid interception is to encrypt communications [124,125].…”

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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“…This attack is brutal to detect, owing to its static nature. When an unknown intruder is involved, the best way to avoid interception is to encrypt communications [124,125].…”

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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“…Below we identify various desirable important characteristics that could be expected of a high performing IDS in our target domain. These are based on our own views and those of other researchers ( [1], [19], [20]). DF1: Adaptivity.…”

Section: A Desirable Characteristicsmentioning

confidence: 99%

“…[18] to enable routing among nodes in low power and lossy networks. RPL is intended to work in LLNs with a low data rate (∼250 kbps) [1], low throughput and high packet loss rate. Moreover, there is an assumption that links would be lossy and occasionally unreachable for an extended period; therefore, when the preferred path is inaccessible RPL is required to provide an alternative route.…”

Section: Df4mentioning

confidence: 99%

“…In LLNs it is likely there is more than one path available for each node to communicate with the border router (root); however, the nodes are only permitted to have one parent (the preferred parent) with regards to the DODAG Objective Function (OF). To build and maintain a DODAG, RPL follows the neighbour discovery procedure using three ICMPv6 control messages [1], [18]: a DODAG Information Object (DIO), a Destination Advertisement Object (DAO), and a DODAG Information Solicitation (DIS). The DIO message initiates the formation of a DODAG.…”

Section: Df4mentioning

confidence: 99%

“…In RPL, intruder alters DODAG control packets' configurations (node's rank, version number, DODAG configuration etc.) to manipulate the confidentiality, integrity and availability (CIA) of data in 6loWPAN [1], [2]. In general, the intruder may disrupt LLN by altering the DIO packet (Sinkhole, Blackhole, Grayhole, and Increase Rank attacks), replaying collected altered control packets (Wormhole and Replay attacks), or flooding control packets (DIS flooding and DIO Suppression attacks).…”

Section: B Rpl Attacksmentioning

confidence: 99%

See 2 more Smart Citations

Reinforcement-Learning-Based IDS for 6LoWPAN

Pasikhani¹,

Clark²,

Gope³

2021

Preprint

Self Cite

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<p>The Routing Protocol for low power Lossy networks (RPL) is a critical operational component of low power wireless personal area networks using IPv6 (6LoWPANs). In this paper we propose a Reinforcement Learning (RL) based IDS to detect various attacks on RPL in 6LoWPANs, including several unaddressed by current research. The proposed scheme can also detect previously unseen attacks and the presence of mobile intruders. The scheme is well suited to the resource constrained environments of our target networks.</p><br>

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On Optimal Configuration of IDS for RPL Resource-Constrained Networks Using Evolutionary Algorithm

Alshahrani

Clark

2022

Proceedings of the Future Technologies Conference (FTC) 2022, Volume 2

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Intrusion Detection Systems in RPL-Based 6LoWPAN: A Systematic Literature Review

Cited by 35 publications

References 100 publications

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

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

Reinforcement-Learning-Based IDS for 6LoWPAN

On Optimal Configuration of IDS for RPL Resource-Constrained Networks Using Evolutionary Algorithm

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