Security of RPL Based 6LoWPAN Networks in the Internet of Things: A Review

Verma, Abhishek; Ranga, Virender

doi:10.1109/jsen.2020.2973677

Cited by 132 publications

(50 citation statements)

References 104 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The best solutions (x j fitness ) are selected based on the fitness values. The value of k 1 is updated using Equation (2). Based on the velocity of PSO as shown in Equation ( 10), the velocity of follower is calculated as given in Equation (12).…”

Section: B) Sa-improved Ssamentioning

confidence: 99%

“…The long-time operation of huge number of devices requires low power network consumption [1]. The Ipv6 based low-power wireless personal area network (6LoWPAN) is a small IoT network that can enable the devices of IoT to operate on a low power [2]. Conventional routing protocols of IoT are not suitable for 6LoWPAN networks due to the lossy and low-power nature of 6LoWPAN networks.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Novel Intrusion Detection System for RPL Based IoT Networks with Bio-Inspired Feature Selection and Ensemble Classifier

Prakash

2021

Preprint

View full text Add to dashboard Cite

Internet of Things (IoT) is the powerful latest trend that allows communications and networking of many sources over the internet. Routing protocol for low power and lossy networks (RPL) based IoT networks may be exposed to many routing attacks due to resource-constrained and open nature of the IoT nodes. Hence, there is a need for network intrusion detection system (NIDS) to protect RPL based IoT networks from routing attacks. The existing techniques for anomaly-based NIDS (ANIDS) subjects to high false alarm rate (FAR). Therefore, a novel bio-inspired voting ensemble classifier with feature selection technique is proposed in this paper to improve the performance of ANIDS for RPL based IoT networks. The proposed voting ensemble classifier combines the results of various base classifiers such as logistic Regression, support vector machine, decision tree, bidirectional long short-term memory and K-nearest neighbor to detect the attacks accurately based on majority voting rule. The optimized weights of base classifiers are obtained by using the feature selection method called simulated annealing based improved salp swarm algorithm (SA-ISSA), which is the hybridization of particle swarm optimization, opposition based learning and salp swarm algorithm. The experiments are performed with RPL-NIDDS17 dataset that contains seven types of attack instances. The performance of the proposed model is evaluated and compared with existing feature selection and classification techniques in terms of accuracy, attack detection rate (ADR), FAR and so on. The proposed ensemble classifier shows better performance with higher accuracy (96.4%), ADR (97.7%) and reduced FAR (3.6%).

show abstract

Section: B) Sa-improved Ssamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Novel Intrusion Detection System for RPL Based IoT Networks with Bio-Inspired Feature Selection and Ensemble Classifier

Prakash

2021

Preprint

View full text Add to dashboard Cite

show abstract

“…In the case of the healthcare sector, IoMT sensors and local devices can be connected to IP networks through 6LoWPAN, allowing the interconnection among a group of sensors. Furthermore, it allows the interconnection of sensors with middleware devices or Internet-connected routers [55][56][57].…”

Section: Lowpanmentioning

confidence: 99%

Security in IoMT Communications: A Survey

Koutras

Stergiopoulos

Dasaklis

et al. 2020

Sensors

103

View full text Add to dashboard Cite

The Internet of Medical Things (IoMT) couples IoT technologies with healthcare services in order to support real-time, remote patient monitoring and treatment. However, the interconnectivity of critical medical devices with other systems in various network layers creates new opportunities for remote adversaries. Since most of the communication protocols have not been specifically designed for the needs of connected medical devices, there is a need to classify the available IoT communication technologies in terms of security. In this paper we classify IoT communication protocols, with respect to their application in IoMT. Then we describe the main characteristics of IoT communication protocols used at the perception, network and application layer of medical devices. We examine the inherent security characteristics and limitations of IoMT-specific communication protocols. Based on realistic attacks we identify available mitigation controls that may be applied to secure IoMT communications, as well as existing research and implementation gaps.

show abstract

“…Several RPL attacks occur through the activities of malicious nodes during the data packet routing among devices [8]. The RPL security has been extensively reviewed in [9][10][11]. Various kinds of RPL attacks have been analyzed, yet most of the studies have not concentrated on the mechanisms of secure RPL.…”

Section: Rpl Securitymentioning

confidence: 99%

Detection and Mitigation of RPL Rank and Version Number Attacks in the Internet of Things: SRPL-RP

Almusaylim

Jhanjhi

Alhumam

2020

Sensors

View full text Add to dashboard Cite

The rapid growth of the Internet of Things (IoT) and the massive propagation of wireless technologies has revealed recent opportunities for development in various domains of real life, such as smart cities and E-Health applications. A slight defense against different forms of attack is offered for the current secure and lightweight Routing Protocol for Low Power and Lossy Networks (RPL) of IoT resource-constrained devices. Data packets are highly likely to be exposed in transmission during data packet routing. The RPL rank and version number attacks, which are two forms of RPL attacks, can have critical consequences for RPL networks. The studies conducted on these attacks have several security defects and performance shortcomings. In this research, we propose a Secure RPL Routing Protocol (SRPL-RP) for rank and version number attacks. This mainly detects, mitigates, and isolates attacks in RPL networks. The detection is based on a comparison of the rank strategy. The mitigation uses threshold and attack status tables, and the isolation adds them to a blacklist table and alerts nodes to skip them. SRPL-RP supports diverse types of network topologies and is comprehensively analyzed with multiple studies, such as Standard RPL with Attacks, Sink-Based Intrusion Detection Systems (SBIDS), and RPL+Shield. The analysis results showed that the SRPL-RP achieved significant improvements with a Packet Delivery Ratio (PDR) of 98.48%, a control message value of 991 packets/second, and an average energy consumption of 1231.75 joules. SRPL-RP provided a better accuracy rate of 98.30% under the attacks.

show abstract

Security of RPL Based 6LoWPAN Networks in the Internet of Things: A Review

Cited by 132 publications

References 104 publications

A Novel Intrusion Detection System for RPL Based IoT Networks with Bio-Inspired Feature Selection and Ensemble Classifier

A Novel Intrusion Detection System for RPL Based IoT Networks with Bio-Inspired Feature Selection and Ensemble Classifier

Security in IoMT Communications: A Survey

Detection and Mitigation of RPL Rank and Version Number Attacks in the Internet of Things: SRPL-RP

Contact Info

Product

Resources

About