RPL rank attack detection using Deep Learning

Choukri, Wijdan; Lamaazi, Hanane; Benamar, Nabil

doi:10.1109/3ict51146.2020.9311983

Cited by 22 publications

(17 citation statements)

References 11 publications

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“…In addition, other studies, such as [7,8,21,45,50,51,53,59,60,62], did not mention the number of features used or selected in their approach. Moreover, two studies [57,60] did not disclose the type of program used in the implementation step, and did not provide information about the dataset and the feature selection technique used.…”

Section: Ac Precision Recall Specificitymentioning

confidence: 99%

“…A DL approach for detecting Rank attack in RPL was introduced by [62]. The proposed approach employs MLP for verifying and classifying the normal and suspicious behavior of network traffic.…”

Section: Rq3: What Are the Prevailing DL Approaches Contributed By Ex...mentioning

confidence: 99%

“…Furthermore, it could be observed that there is a lack in the information provided in the reviewed studies, such as [65], where the authors did not disclose the feature selection techniques used in their works. In addition, [62] did not clarify the number of features used in their solution. Meanwhile, [58] generated a new dataset and publicly published it for the other researchers.…”

Section: Rq3: What Are the Prevailing DL Approaches Contributed By Ex...mentioning

confidence: 99%

“…Consequently, the frequent reconstruction increases network traffic and causes an impact on critical network factors, such as lifetime, availability, and energy efficiency[56,76]. • DIS Flooding Attack: This attack occurs when one or more malicious nodes periodically send DIS messages to neighboring nodes within their transmission range, and upon receiving of DIS message, the trickle timers of the victim node(s) would reset, and this process continues until the power resources of the victim node(s) depleted, crashing the network[62]. • DDoS Flooding Attack: In a DDoS flooding attack, various malicious nodes target the network nodes with vast amounts of traffic to interrupt the normal operation of the network services.…”

mentioning

confidence: 99%

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A Systematic Literature Review on Machine and Deep Learning Approaches for Detecting Attacks in RPL-Based 6LoWPAN of Internet of Things

Al-Amiedy

Anbar

Belaton

et al. 2022

Sensors

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The IETF Routing Over Low power and Lossy network (ROLL) working group defined IPv6 Routing Protocol for Low Power and Lossy Network (RPL) to facilitate efficient routing in IPv6 over Low-Power Wireless Personal Area Networks (6LoWPAN). Limited resources of 6LoWPAN nodes make it challenging to secure the environment, leaving it vulnerable to threats and security attacks. Machine Learning (ML) and Deep Learning (DL) approaches have shown promise as effective and efficient mechanisms for detecting anomalous behaviors in RPL-based 6LoWPAN. Therefore, this paper systematically reviews and critically analyzes the research landscape on ML, DL, and combined ML-DL approaches applied to detect attacks in RPL networks. In addition, this study examined existing datasets designed explicitly for the RPL network. This work collects relevant studies from five major databases: Google Scholar, Springer Link, Scopus, Science Direct, and IEEE Xplore® digital library. Furthermore, 15,543 studies, retrieved from January 2016 to mid-2021, were refined according to the assigned inclusion criteria and designed research questions resulting in 49 studies. Finally, a conclusive discussion highlights the issues and challenges in the existing studies and proposes several future research directions.

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Section: Ac Precision Recall Specificitymentioning

confidence: 99%

Section: Rq3: What Are the Prevailing DL Approaches Contributed By Ex...mentioning

confidence: 99%

Section: Rq3: What Are the Prevailing DL Approaches Contributed By Ex...mentioning

confidence: 99%

mentioning

confidence: 99%

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A Systematic Literature Review on Machine and Deep Learning Approaches for Detecting Attacks in RPL-Based 6LoWPAN of Internet of Things

Al-Amiedy

Anbar

Belaton

et al. 2022

Sensors

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“…Rank increase attacks: these attacks include purposefully raising an RPL node's rank value to construct a network loop. A rank value in an RPL network is determined by each node and corresponds to the root node's location in the graph structure [101,102]. The nodes' rank in the downward paths will be continuously increasing to stabilize the DODAG graph structure.…”

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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