Detection and Prevention Analysis of Wormhole Attack in Wireless Sensor Network

Dwivedi, Rajendra Kumar; Sharma, Prachi; Kumar, Rakesh

doi:10.1109/confluence.2018.8442601

Cited by 14 publications

(5 citation statements)

References 8 publications

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“…Several detection and prevention methods are explained. In this paper [10], some methods give outstanding performance, and some have a few shortcomings. There are several types of methods and techniques to detect the Sybil attack.…”

Section: Related Workmentioning

confidence: 99%

“…Previous studies [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15] describe Wireless Sensor Networks, Applications of Wireless Sensor Networks, and different types of attacks that occur in the layers of the Wireless Sensor Network and how to detect those attacks using various methods. Different types of methods for detecting wormhole attacks and Sybil attacks that can occur in various types of networks such as Wireless Sensor Networks and Ad hoc Networks are explained [4].…”

Section: Introductionmentioning

confidence: 99%

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Detection of Network Layer Attacks in Wireless Sensor Network

Annapurna¹,

Anusha²,

Varsha³

et al. 2023

AJCT

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The Wireless Sensor Network (WSN) technology is being used in a huge number of monitoring applications. It consists of a large number of sensor nodes with limited battery life. These sensor devices are deployed randomly in a sensor zone to collect the data. But these are threatened and attacked by several malicious behaviors caused by some nodes, which result in security attacks. Several security attacks occur in different layers of the wireless sensor network. Due to these attacks, confidential information can be stolen by attackers or unauthorized users, which can cause several problems for authorized users. Cyber-attacks by sending large data packets that deplete computer network service resources by using multiple computers when attacking are called wormhole and Sybil attacks. It is important to identify these attacks to prevent further damage. To overcome these problems, we use a prediction module that consists of various machine learning algorithms to find the bestperforming algorithm. we use XGBoost, Adaboost, Random Forest, and KNN algorithms. To train these algorithms, we have used the WHASA dataset which contains 10 different attacks of the VANET environment and benign (normal) class. By using these algorithms classification of attacks can be done which occur on the computer network service that is " normal " access or access under " attack " by Wormhole and Sybil attack as an output.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Detection of Network Layer Attacks in Wireless Sensor Network

Annapurna¹,

Anusha²,

Varsha³

et al. 2023

AJCT

View full text Add to dashboard Cite

show abstract

“…Mohit et al, reviewed different techniques for the detection of wormhole attacks, a significantly dangerous attack affecting the mission of the network [16]. Alomar et al, focused on the security of wormhole detection techniques in WSNs by compromising their energy efficiency [17]. Goyal et al, reviewed schemes for recognizing a wormhole attack in IoT networks [18].…”

Section: Introductionmentioning

confidence: 99%

“…Detection and prevention analysis of wormhole attacks in wireless sensor networks Kumar et al [17] presented a comparative analysis of several techniques, including reputation-based routing, Packet leashes, Beacon nodes, LITEWORP, and algorithms using active nodes. However, the study did not include the strengths and limitations of the existing schemes.…”

mentioning

confidence: 99%

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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“…Among the above attacks, the wormhole attack is one of the most harmful attacks, changing network topology structure and reducing node positioning accuracy [17] [18]. At present, Distance Vector Hop (DV-Hop) algorithm is one of the leading technologies of node localization.…”

Section: Introductionmentioning

confidence: 99%

A novel secure DV-Hop localization algorithm against wormhole attacks

et al. 2022

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IoT era and its ubiquitous sensing raises serious security challenges such as wormhole attacks. Given these attacks may affect the location determination of the employed sensors, security can be seriously compromised. The most common and serious attack is the single wormhole one, which is the focus of this paper. One of the most employed algorithms to approach the sensor location determination is the Distance Vector Hop (DV-Hop) algorithm, which can stillbe seriously affected from wormhole attacks. To overcome the challenges of this algorithm, this article proposes a novel secure DV-Hop localization algorithm against wormhole attack (ANDV-Hop), where beacon nodes delegate their attacked neighboring nodes to broadcast data messages, and the intersection of communication range of these neighboring nodes does include wormhole nodes. For implicit wormhole attacks, close nodes to the wormhole node are selected in order to broadcast data messages, whilst the nodes within attack range remove beacon nodes at the other end of the link from the neighboring list. For explicit wormhole attack, the algorithm employs a trust model that calculates the comprehensive trust value which is obtained via a selection reward/punish coefficient, where the selected ones within the intersection zone are considered as rewarded, whilst the ones to be removed, classified as punished Experimental results show that the proposed algorithm improves detection success rate, reduces relative localization error and energy loss, showing effectiveness and reliability.

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Detection and Prevention Analysis of Wormhole Attack in Wireless Sensor Network

Cited by 14 publications

References 8 publications

Detection of Network Layer Attacks in Wireless Sensor Network

Detection of Network Layer Attacks in Wireless Sensor Network

AI-Based Wormhole Attack Detection Techniques in Wireless Sensor Networks

A novel secure DV-Hop localization algorithm against wormhole attacks

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