Proposing a Hybrid RPL Protocol for Rank and Wormhole Attack Mitigation using Machine Learning

Fatima-tuz-Zahra,; Jhanjhi, N. Z.; Brohi, Sarfraz Nawaz; Malik, Nazir Ahmed; Humayun, Mamoona

doi:10.1109/iccis49240.2020.9257607

Cited by 43 publications

(13 citation statements)

References 24 publications

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“…The research of Ref. [27] presented a hybrid RPL protocol for mitigating wormhole attacks with high DA and using less computation power. It uses a support vector machine, a supervised machine learning algorithm for detecting intruders.…”

Section: Artificial Immune Systems and Machine Learning-based Systemsmentioning

confidence: 99%

“…The research of Ref. [27] presented an SVM-based scheme with an RPL protocol which provides high detection accuracy and requires less computation power; however, the computational complexity of the SVM is high [73].…”

Section: Performance Analysis 41 Critical Analysismentioning

confidence: 99%

“…Artificial intelligence-and ML-based schemes can be used to solve all the identified state-of-the-art problems [26][27][28][29][30][31][32][33][34][35][36]. The first challenge, as mentioned in Table 7, is neighbourbased discovery schemes.…”

Section: Ai-and Ml-based Schemes As Optimal Solutions To State-of-the...mentioning

confidence: 99%

“…The milestone of this machine learning scheme is the reduced communication overhead, which overcomes the state-of-the-art problem of RTTbased schemes. Another deep learning approach that uses RTT with LSTM [27] provides high detection accuracy with high PDR. LSTM is a deep neural network that works very well in complex conditions.…”

Section: Ai-and Ml-based Schemes As Optimal Solutions To State-of-the...mentioning

confidence: 99%

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AI-Based Wormhole Attack Detection Techniques in Wireless Sensor Networks

et al. 2022

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The popularity of wireless sensor networks for establishing different communication systems is increasing daily. A wireless network consists of sensors prone to various security threats. These sensor nodes make a wireless network vulnerable to denial-of-service attacks. One of them is a wormhole attack that uses a low latency link between two malicious sensor nodes and affects the routing paths of the entire network. This attack is brutal as it is resistant to many cryptographic schemes and hard to observe within the network. This paper provides a comprehensive review of the literature on the subject of the detection and mitigation of wormhole attacks in wireless sensor networks. The existing surveys are also explored to find gaps in the literature. Several existing schemes based on different methods are also evaluated critically in terms of throughput, detection rate, low energy consumption, packet delivery ratio, and end-to-end delay. As artificial intelligence and machine learning have massive potential for the efficient management of sensor networks, this paper provides AI- and ML-based schemes as optimal solutions for the identified state-of-the-art problems in wormhole attack detection. As per the author’s knowledge, this is the first in-depth review of AI- and ML-based techniques in wireless sensor networks for wormhole attack detection. Finally, our paper explored the open research challenges for detecting and mitigating wormhole attacks in wireless networks.

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Section: Artificial Immune Systems and Machine Learning-based Systemsmentioning

confidence: 99%

Section: Performance Analysis 41 Critical Analysismentioning

confidence: 99%

Section: Ai-and Ml-based Schemes As Optimal Solutions To State-of-the...mentioning

confidence: 99%

Section: Ai-and Ml-based Schemes As Optimal Solutions To State-of-the...mentioning

confidence: 99%

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AI-Based Wormhole Attack Detection Techniques in Wireless Sensor Networks

et al. 2022

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“…Artificial intelligence and machine learning systems [11][12][13][14] are suggested as solutions to the wormhole attack. The wormhole attacks of the nodes under the uniform, gaussian, poison, exponential, gamma, and beta distribution functions are detected by the artificial neural networks 15 .…”

Section: Introductionmentioning

confidence: 99%

An adaptive technique for wormhole attack detection in MANET using quantized ad-hoc on-demand multipath distance vector routing protocol

Kumar

2023

International Conference on Mathematical and Statistical Physics, Computational Science, Education, and Communication (ICMSCE 2

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In a self-evolving network, security is an essential requirement, and ensuring it in any circumstance is the most difficult task because the network is very dynamic and lacks reliable centralised monitoring. MANETs are much more vulnerable to security attacks when compared to wired networks. There are two steps in this research: the routing stage and the wormhole detection stage. Quantum walk and reinforcement learning are used to improve the results of ad hoc on-demand multipath distance vector routing technology. Quantum walk detects the moving nodes from the area and updates it to the reinforcement learning table. The proposed model is faster since the adaptive African vulture optimization is employed for path selection. This optimization creates an ideal path from source to destination using the data obtained by the routing protocol and reinforcement learning. Round trip time and packet delivery rate are key elements in the detecting phase. This parameter establishes a threshold point for classifying suspicious and normal nodes. Depending on the type of attack, the node will either be terminated or recovered if it turns malicious. Performance indicators including the proportion of packet delivery ratio (98%), end-to-end latency (23.91 sec), packet loss (4.75%), energy consumption (57J), throughput (408/sec), and average E2E delay are examined and compared with existing models to validate the efficacy of the proposed model.

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