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

Seyfollahi, Ali; Ghaffari, Ali

doi:10.1155/2021/8414503

Cited by 30 publications

(13 citation statements)

References 168 publications

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“…[18] proposed a service provider affordability via energy optimization that could optimize service provider network operational cost to sustainability and affordable services between both customers and service providers. In LLNs, RPL (Routing Protocol for LLNs) [19] is the first routing protocol proposed by IETF, where some aspects still need to be improved. Hwang et al.…”

Section: Related Workmentioning

confidence: 99%

“…In SoDIP6, Dawadi et al [18] proposed a service provider affordability via energy optimization that could optimize service provider network operational cost to sustainability and affordable services between both customers and service providers. In LLNs, RPL (Routing Protocol for LLNs) [19] is the first routing protocol proposed by IETF, where some aspects still need to be improved. Hwang et al [20] proposed a Wireless Shortest Path Heuristic (W-SPH) mechanism to construct a multicast tree, which could build an effective routing topology for multicast traffic and yield higher energy efficiency.…”

Section: Routing Protocols In Ipv6 Networkmentioning

confidence: 99%

“…(c) Calculate the bandwidth capacity weights of each disjoint link state as Equation ( 18). (d) Calculate the weighted reliability index of each route as Equation (19).…”

Section: Reliability Index Calculationmentioning

confidence: 99%

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An efficient and reliable service customized routing mechanism based on deep learning in IPv6 network

Wang

et al. 2022

IET Communications

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Best-effort service model of traditional routing is gradually hard to meet the personalized demands under the rapid development of network technologies (e.g. 5G and IPv6). Therefore, service customization should be considered. In this work, a service customized routing mechanism based on deep learning in IPv6 network is proposed, which includes deep learning-based service customization module, reliability evaluation module, and routing calculation module. The first module uses neural network to learn the complex service customization function, which can quickly output win-win customized service strategies based on user demands. The second module can quantify the reliability of service routing paths, where not only the link status of IPv6 Neighbor Unreachable Detection (NUD) is considered, but also propose link performance weights to ensure the reliability of differentiated service performance. The third module uses the gray wolf optimization algorithm to calculate an optimal routing path to forward services with the customized strategies as the constraints and the maximum reliability and minimum cost as the goal. Finally, the mechanism is tested on the IPv6 Source Address Validation Improvement (SAVI) platform, which can reduce the execution time by 12.25% and improve the average routing reliability, user and ISP satisfaction by 9.0%, 40.45% and 7.4%, respectively.

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Section: Related Workmentioning

confidence: 99%

Section: Routing Protocols In Ipv6 Networkmentioning

confidence: 99%

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An efficient and reliable service customized routing mechanism based on deep learning in IPv6 network

Wang

et al. 2022

IET Communications

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“…Seyfollahi et al 12 reviewed machine learning techniques used in designing intrusion detection systems for the Low-Power and Lossy Networks (RPL) protocol. The study also identified open issues and challenges related to their study's domain.…”

Section: Related Workmentioning

confidence: 99%

Towards achieving lightweight intrusion detection systems in Internet of Things, the role of incremental machine learning: A systematic literature review

Agbedanu

Musabe²,

Rwigema

et al. 2022

F1000Res

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While the benefits of IoT cannot be overstated, its computational constraints make it challenging to deploy security methodologies that have been deployed in traditional computing systems. The benefits and computational constraints have made IoT systems attractive to cyber-attacks. One way to mitigate these attacks is to detect them. In this study, a Systematic Literature Review (SLR) has been conducted to analyze the role of incremental machine learning in achieving lightweight intrusion detection for IoT systems. The study analyzed existing incremental machine learning approaches used in designing intrusion detection systems for IoT ecosystems, emphasizing the incremental methods used in detecting intrusions, the datasets used to evaluate these methods, and how the method achieves lightweight status. The SLR outlined the contributions of each study, focusing on their strengths and gaps, the datasets used, and the incremental machine learning model used. This study revealed that incremental learning approaches in detecting intrusion in IoT systems are in their infant stage. Over 12 years, from 2010 to 2022, a total of twenty-one (21) studies were carried out in IDSs using incremental machine learning, with eight (8) studies carried out in IoT systems. In addition to reviewing the literature, we offer suggestions for improving existing solutions and achieving lightweight IDS for IoT systems. We also discussed some problems with making lightweight IDS for IoT systems and areas where more research could be done in the future.

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“…However, the resource-limited nature of IoT devices and critical smart infrastructures motivate hackers to launch various attacks within the RPL-IIoT scenario, and RPL security is paramount. A common method of RPL-IIoT attack detection is to employ a machine learning (ML)-based intrusion detection system (IDS) [ 3 , 4 ]. However, most traditional ML-based IDS solutions are centralised and do not have the flexibility to detect the variety and full range of RPL attacks on IIoT applications [ 5 ].…”

Section: Introductionmentioning

confidence: 99%

Customised Intrusion Detection for an Industrial IoT Heterogeneous Network Based on Machine Learning Algorithms Called FTL-CID

Abosata

Al‐Rubaye

Tsourdos

2022

Sensors

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Technological breakthroughs in the Internet of Things (IoT) easily promote smart lives for humans by connecting everything through the Internet. The de facto standardised IoT routing strategy is the routing protocol for low-power and lossy networks (RPL), which is applied in various heterogeneous IoT applications. Hence, the increase in reliance on the IoT requires focus on the security of the RPL protocol. The top defence layer is an intrusion detection system (IDS), and the heterogeneous characteristics of the IoT and variety of novel intrusions make the design of the RPL IDS significantly complex. Most existing IDS solutions are unified models and cannot detect novel RPL intrusions. Therefore, the RPL requires a customised global attack knowledge-based IDS model to identify both existing and novel intrusions in order to enhance its security. Federated transfer learning (FTL) is a trending topic that paves the way to designing a customised RPL-IoT IDS security model in a heterogeneous IoT environment. In this paper, we propose a federated-transfer-learning-assisted customised distributed IDS (FT-CID) model to detect RPL intrusion in a heterogeneous IoT. The design process of FT-CID includes three steps: dataset collection, FTL-assisted edge IDS learning, and intrusion detection. Initially, the central server initialises the FT-CID with a predefined learning model and observes the unique features of different RPL-IoTs to construct a local model. The experimental model generates an RPL-IIoT dataset with normal and abnormal traffic through simulation on the Contiki-NG OS. Secondly, the edge IDSs are trained using the local parameters and the globally shared parameters generated by the central server through federation and aggregation of different local parameters of various edges. Hence, transfer learning is exploited to update the server’s and edges’ local and global parameters based on relational knowledge. It also builds and customised IDS model with partial retraining through local learning based on globally shared server knowledge. Finally, the customised IDS in the FT-CID model enforces the detection of intrusions in heterogeneous IoT networks. Moreover, the FT-CID model accomplishes high RPL security by implicitly utilising the local and global parameters of different IoTs with the assistance of FTL. The FT-CID detects RPL intrusions with an accuracy of 85.52% in tests on a heterogeneous IoT network.

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

Cited by 30 publications

References 168 publications

An efficient and reliable service customized routing mechanism based on deep learning in IPv6 network

An efficient and reliable service customized routing mechanism based on deep learning in IPv6 network

Towards achieving lightweight intrusion detection systems in Internet of Things, the role of incremental machine learning: A systematic literature review

Customised Intrusion Detection for an Industrial IoT Heterogeneous Network Based on Machine Learning Algorithms Called FTL-CID

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