Energy Efficient Approach to Detect Sinkhole Attack Using Roving IDS in 6LoWPAN Network

Bhale, Pradeepkumar; Dey, Sukanta; Biswas, Santosh; Nandi, Sukumar

doi:10.1007/978-3-030-37484-6_11

Cited by 14 publications

(12 citation statements)

References 25 publications

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“…However, these studies have been limited in contribution because of the datasets used. Some of them (Al-Hadhrami & Hussain, 2020;Bhale, Dey, Biswas & Nandi, 2020;Cakir, Toklu & Yalcin, 2020;Cakir & Yalcin, 2021;Mbarek, Ge & Pitner, 2020;Sharma, Elmiligi, Gebali & Verma, 2019;Verma & Ranga, 2020;Yavuz, Ünal & Gül, 2018) use simulation data for developing their models but such data may not be very convenient to develop machine learning models especially in realistic attack scenarios because simulators simulate a certain behavior and the data generated by them may not be very realistic. Some other works (Mounica, Vijayasaraswathi & Vasavi, 2021;Rezvy, Luo, Petridis, Lasebae & Zebin, 2019), on the other hand, use outdated network datasets which consist of generic network attacks.…”

Section: Motivationmentioning

confidence: 99%

Data driven intrusion detection for 6LoWPAN based IoT systems

Örs

Lévi

2023

Ad Hoc Networks

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Section: Motivationmentioning

confidence: 99%

Data driven intrusion detection for 6LoWPAN based IoT systems

Örs

Lévi

2023

Ad Hoc Networks

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“…Every CH's datum is instantly forwarded to the BS [26]. Various surveys on energy use, energy gaps and attacks on RPL and LLP systems can be found in [27][28][29][30]. The glowworm swarm-based approach cast-off energy-based transmission strategy has been presented to decrease energy consumption caused by control overhead [31].…”

Section: Literature Reviewmentioning

confidence: 99%

Clustering Based Optimal Cluster Head Selection Using Bio-Inspired Neural Network in Energy Optimization of 6LowPAN

et al. 2022

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The goal of today’s technological era is to make every item smart. Internet of Things (IoT) is a model shift that gives a whole new dimension to the common items and things. Wireless sensor networks, particularly Low-Power and Lossy Networks (LLNs), are essential components of IoT that has a significant influence on daily living. Routing Protocol for Low Power and Lossy Networks (RPL) has become the standard protocol for IoT and LLNs. It is not only used widely but also researched by various groups of people. The extensive use of RPL and its customization has led to demanding research and improvements. There are certain issues in the current RPL mechanism, such as an energy hole, which is a huge issue in the context of IoT. By the initiation of Grid formation across the sensor nodes, which can simplify the cluster formation, the Cluster Head (CH) selection is accomplished using fish swarm optimization (FSO). The performance of the Graph-Grid-based Convolution clustered neural network with fish swarm optimization (GG-Conv_Clus-FSO) in energy optimization of the network is compared with existing state-of-the-art protocols, and GG-Conv_Clus-FSO outperforms the existing approaches, whereby the packet delivery ratio (PDR) is enhanced by 95.14%.

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“…The dependence on permission is one of the benefits of this approach that creates various rules for every network area. Lee et al [56] take a regional network organization approach. By creating a column of observer nodes, they use a combination technique.…”

Section: Placement Technique For Hybrid Intrusion Detectionmentioning

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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Energy Efficient Approach to Detect Sinkhole Attack Using Roving IDS in 6LoWPAN Network

Cited by 14 publications

References 25 publications

Data driven intrusion detection for 6LoWPAN based IoT systems

Data driven intrusion detection for 6LoWPAN based IoT systems

Clustering Based Optimal Cluster Head Selection Using Bio-Inspired Neural Network in Energy Optimization of 6LowPAN

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

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