SMTrust: Proposing Trust-Based Secure Routing Protocol for RPL Attacks for IoT Applications

Muzammal, Syeda Mariam; Murugesan, Raja Kumar; Jhanjhi, N. Z.; Jung, Low Tang

doi:10.1109/icci51257.2020.9247818

Cited by 26 publications

(7 citation statements)

References 25 publications

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“…All these crucial considerations demonstrate the importance of enhancing the resilience of RPL networks against internal routing attacks, particularly the VN attack. Recently, there have been some research efforts toward addressing such a fundamental RPL security challenge of VN attack mitigation [23], [24], [25], [26], [27], [28], [29], [30], [31], [32], [33], [34], [35]. However, most of the proposed solutions were based on limited design aspects such as the need for additional entities, heavy node-local processing, and further communication establishment.…”

Section: Trillion Amentioning

confidence: 99%

“…The simulation results indicated the ability of the proposed approach to achieve high QoS performance and an acceptable accuracy rate. A different trust-based solution was also proposed in [25] to address VN attacks in addition to other routing attacks in RPL networks. It is based on the calculation of node trustworthiness using essential trust metrics.…”

Section: Related Workmentioning

confidence: 99%

“…However, there are still certain limitations that have not been effectively addressed. It can be seen that the existing approaches for detecting and mitigating VN attacks imposed additional entities [23], [33], [34], [35], node-local processing [24], [25], [26], [30], [31], [32], or communication overhead [27], [28], [33], [34], [35]. Therefore, such limitations need further consideration toward enriching the security of the protocol.…”

Section: Related Workmentioning

confidence: 99%

See 2 more Smart Citations

A Lightweight Scheme for Mitigating RPL Version Number Attacks in IoT Networks

Alsukayti

Singh²

2022

IEEE Access

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Internet of Things (IoT) systems incorporate a multitude of resource-limited devices typically interconnected over Low Power and Lossy Networks (LLNs). Robust IP-based network routing among such constrained IoT devices can be effectively realized using the IPv6 Routing Protocol for LLN (RPL) which is an IETF-standardized protocol. The RPL design features a topology maintenance mechanism based on a version numbering system. However, such a design property makes it easy to initiate Version Number (VN) attacks targeting the stability, lifetime, and performance of RPL networks. Thus the wide deployment of RPL-based IoT networks would be hindered significantly unless internal routing attacks such as the VN attacks are efficiently addressed. In this research work, a lightweight and effective detection and mitigation solution against RPL VN attacks is introduced. With simple modifications to the RPL functionality, a collaborative and distributed security scheme is incorporated into the protocol design (referred to as CDRPL). As the experimental results indicated, it provides a secure and scalable solution enhancing the resilience of the protocol against simple and composite VN attacks in different experimental setups. CDRPL guaranteed fast and accurate attack detection as well as quick topology convergence upon any attack attempt. It also efficiently maintained network stability, control traffic overhead, QoS performance, and energy consumption during different scenarios of the VN attack. Compared to other similar approaches, CDRPL yields better performance results with lightweight node-local processing, no additional entities, and less communication overhead.

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Section: Trillion Amentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

A Lightweight Scheme for Mitigating RPL Version Number Attacks in IoT Networks

Alsukayti

Singh²

2022

IEEE Access

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“…Further, the author used deep learning-based for vulnerability discovery and deep learning is suitable to deal with problems. As the author took some guiding principles to apply deep learning to vulnerability discovery and find representations of software programs that are suitable for deep learning [25]. The researcher used code gadgets to symbolize platforms and transformed them into paths.…”

Section: Bsummary Of Other Associated Research Papersmentioning

confidence: 99%

Using ML and Data-Mining Techniques in Automatic Vulnerability Software Discovery

2021

IJATCSE

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Today’s age is Machine Learning (ML) and Data-Mining (DM) Techniques, as both techniques play a significant role in measuring vulnerability prediction accuracy. In the field of computer security, vulnerability is a fault that might be exploited as a risk artist that performs unlawful activities inside computer security. The attackers have several different fitting tools and they are taking advantage to operate software illegally and are using it for getting self-profit. Additionally, that helps to expose and identify the violence external. Weakness management remains a repeating exercise to identify, remediating, and justifying weaknesses. These exercises normally send software faults in computing security. The meaning of using weakness with the same risk might go to misperception. It is possible to have a major effect because of possible stability and the window of weakness presented a risk hole in the software and required to fruitfully finish and smoothly operate. A security room has to be set up (zero-day invaders). Software Security Faults stand serious among unavoidable complications in the realm of computer risk. In this study, we have provided a comprehensive review of three book chapters, more than a hundred research articles papers, and several associated papers of different work that have been studied within the capacity of SVA and discovery applying ML and data-mining techniques. The earlier work has been thoroughly read and an adequately comprehensive summary has been provided in table-1. ML techniques that can professionally handle these attacks and we expect the net result of this survey article to help indesigning the new detection model for identifying the above-mentioned attacks

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“…In (13) proposed an algorithm to secure the RPL protocol with trust parameters and mobility parameters that allows only the trusted nodes to take part in data transmission. This algorithm failed to identify the behavior of the malicious node and also to identify the rank attacks.…”

Section: Introductionmentioning

confidence: 99%

Secured Technique to Detect and Avoid Malicious Nodes in Internet of Things

Newton¹,

Samuel²

2022

IJST

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Objectives: To detect rank attacks during topology establishment and updated the RPL Destination Oriented Directed Acyclic Graph (DODAG) formation algorithm. The algorithm's distributed module runs across all participating nodes, while the centralized module runs in the sink. Methods: The integrity and authenticity of control messages transmitted among two nodes and the sink are verified using a lightweight Hashed Message Authentication Code -Light-weight One-way Cryptographic Hash Algorithm (HMAC-LOCHA). The Secured Technique to Detect and Avoid Malicious Nodes (STDAMN) technique is proposed to overcome the rank attack of the nodes. Findings:The proposed scheme STDAMN outperforms the LEADER and SBIDS schemes when considering 50% malicious nodes, the accuracy rate of STDAMN is 3% higher than LEADER and 17% higher than SBIDS in Security mode whereas it is 4% and 14% higher in non-security mode respectively in the decreased rank attack. Again considering 50% malicious nodes, the accuracy rate of STDAMN is 2% higher than LEADER and 13% higher than SBIDS in with-Security mode whereas it is 2.2% and 16.1% higher in without-security mode respectively in the increased rank attack. Also indeed, the false positive rate for STDAMN is lower by 72.5% and 72%, 15.3% and 21.2% whereas the false negative rate for STDAMN is lower by 77.2% and 62.1%, 32.5%, and 39.5% on average for with-security and without-security respectively than LEADER and SBIDS in the decreased rank attack. Novelty: This paper presents a rank attack detection approach for non-storing mode RPL used in IoT to cope with both increased and decreased rank attacks to address this issue. The performance of the suggested technique is assessed both conceptually and through simulation using the Contiki-based Cooja simulator. The proposed technique surpasses state-of-the-art rank attack detection techniques in terms of detection accuracy and false positive/negative rate while maintaining acceptable network performance, according to simulation results.

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SMTrust: Proposing Trust-Based Secure Routing Protocol for RPL Attacks for IoT Applications

Cited by 26 publications

References 25 publications

A Lightweight Scheme for Mitigating RPL Version Number Attacks in IoT Networks

A Lightweight Scheme for Mitigating RPL Version Number Attacks in IoT Networks

Using ML and Data-Mining Techniques in Automatic Vulnerability Software Discovery

Secured Technique to Detect and Avoid Malicious Nodes in Internet of Things

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