Performance Evaluation of a Wormhole Detection Method using Round-Trip Times and Hop Counts in RPL-Based 6LoWPAN Networks

Samuel, Charisma; Alvarez, Brian Martinez; Ribera, Eric Garcia; Ioulianou, Philokypros P.

doi:10.1109/csndsp49049.2020.9249612

Cited by 8 publications

(3 citation statements)

References 14 publications

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“…The experimental results presented in [30] demonstrated how RPL networks experienced a high delay, packet loss, and network overhead when being under blackhole attacks. The experimental study in [31] showed the adverse impact of the wormhole attack on the power consumption and network overhead of RPL networks. The same attack was investigated in [32] over a real-testbed setup.…”

Section: Related Workmentioning

confidence: 99%

RPL-Based IoT Networks under Simple and Complex Routing Security Attacks: An Experimental Study

Alsukayti

Alreshoodi

2023

Applied Sciences

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Effective security support still remains a challenge even for a standardized Internet of Things network protocol such as the IPv6 Routing Protocol for Low-Power and Lossy Networks (RPL). It provides limited protection against external security attacks but stays highly vulnerable to internal routing attacks. The inherent RPL design of RPL, particularly its topology establishment and maintenance mechanism, makes it easy to initiate such kinds of attacks which target overall network performance and topology stability. Establishing a firm and practical understanding of the impacts of these attacks on RPL networks is still critically needed for further investigation. This is more demanding when considering varying-scale RPL deployments targeted by complex attack scenarios. In this research work, an extensive experimental study of these critical RPL routing attacks considering simple-to-complex attack scenarios in varying-scale RPL network setups is presented. It provides a practical contribution toward experimentally understanding the effectiveness of internal routing attacks when targeting RPL-based IoT networks. The main objective is to provide future research works with a practical reference to the effectiveness of these security attacks and the overall performance of RPL networks under routing attacks. The results indicate the adverse impacts of routing attacks on the overall performance of RPL networks. Even in simple attack scenarios, it was found that the attacked networks experienced noticeable degradation in QoS performance and topology stability. In addition, the attacks incurred considerable increases in energy consumption and control traffic overhead. These were more evident in large-scale experimental setups and also under composite and hybrid routing attacks. QoS performance and topology stability degraded by more than 90% whereas energy consumption and network overhead increased by more than 200%.

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Section: Related Workmentioning

confidence: 99%

RPL-Based IoT Networks under Simple and Complex Routing Security Attacks: An Experimental Study

Alsukayti

Alreshoodi

2023

Applied Sciences

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“…On the other hand, routing attacks focus on distorting the routing information in order to cause a negative impact on the network. Examples of these attacks are version number attack, blackhole attack, greyhole attack, wormhole attack, and rank decrease attack [24,[27][28][29]. To detect the aforementioned attacks, IDSes are being studied and tested on RPL-based networks.…”

Section: Introductionmentioning

confidence: 99%

An Intrusion Detection System for RPL-Based IoT Networks

et al. 2022

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The Internet of Things (IoT) has become very popular during the last decade by providing new solutions to modern industry and to entire societies. At the same time, the rise of the industrial Internet of Things (IIoT) has provided various benefits by linking infrastructure around the world via sensors, machine learning, and data analytics. However, the security of IoT devices has been proven to be a major concern. Almost a decade ago, the IPv6 Routing Protocol for Low-Power and Lossy Networks (RPL) was designed to handle routing in IoT and IIoT. Since then, numerous types of attacks on RPL have been published. In this paper, a novel intrusion detection system (IDS) is designed and implemented for RPL-based IoT. The objective is to perform an accurate and efficient detection of various types of routing and denial-of-service (DoS) attacks such as version number attack, blackhole attack, and grayhole attack, and different variations of flooding attacks such as Hello flood attack, DIS attack, and DAO insider attack. To achieve this, different detection strategies are combined, taking advantage of the strengths of each individual strategy. In addition, the proposed IDS is experimentally evaluated by performing a deep analysis of the aforementioned attacks in order to study the impact caused. This evaluation also estimates the accuracy and effectiveness of the IDS performance when confronted with the considered attacks. The obtained results show high detection accuracy. Furthermore, the overhead introduced in terms of CPU usage and power consumption is negligible. In particular, the CPU usage overhead is less than 2% in all cases, whereas the average power consumption increase is no more than 0.5%, which can be considered an insignificant impact on the overall resource utilisation.

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“…Other important attacks in RPL networks are wormhole and grayhole attacks. They can be detected using methods that rely on packet counts and round-trip times [5] as well as by implementing heartbeatbased protocols [6]. We have also designed and implemented an IDS prototype for protecting RPL networks and devices from battery-drain DoS attacks using rate thresholds [7].…”

Section: Introductionmentioning

confidence: 99%

ML-based Detection of Rank and Blackhole Attacks in RPL Networks

Ioulianou

Shahandashti

2022

2022 13th International Symposium on Communication Systems, Networks and Digital Signal Processing (CSNDSP)

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Although IoT security is a field studied extensively, recent attacks such as BotenaGo show that current security solutions cannot effectively stop the spread of IoT attacks. Machine Learning (ML) techniques are promising in improving protection against such attacks. In this work, three supervised ML algorithms are trained and evaluated for detecting rank and blackhole attacks in RPL-based IoT networks. Extensive simulations of the attacks are implemented to create a dataset and appropriate fields are identified for training the ML model. We use Google AutoML and Microsoft Azure ML platforms to train our model. Our evaluation results show that ML techniques can be effective in detecting rank and blackhole attacks, achieving a precision of 93.3%.

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Performance Evaluation of a Wormhole Detection Method using Round-Trip Times and Hop Counts in RPL-Based 6LoWPAN Networks

Cited by 8 publications

References 14 publications

RPL-Based IoT Networks under Simple and Complex Routing Security Attacks: An Experimental Study

RPL-Based IoT Networks under Simple and Complex Routing Security Attacks: An Experimental Study

An Intrusion Detection System for RPL-Based IoT Networks

ML-based Detection of Rank and Blackhole Attacks in RPL Networks

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