Survey of wormhole attack in wireless sensor networks

Ghugar, Umashankar; Pradhan, Jayaram

doi:10.11591/csit.v2i1.p33-42

Cited by 13 publications

(16 citation statements)

References 31 publications

(39 reference statements)

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“…The attacker node in the gray hole receives data from the source node, but it is not sent to the target node [16]. The attacker node just drops some packets [24]. The following two scenarios are reviewed to determine whether the data transmitted is normal or not.…”

Section: Gray Holementioning

confidence: 99%

Intrusion prevention system using convolutional neural network for wireless sensor network

Chandre

Mahalle²,

Shinde³

2022

IJ-AI

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Now-a-days, there is exponential growth in the field of wireless sensor network. In wireless sensor networks (WSN’s), most of communication happen through wireless media hence probability of attacks increases drastically. With the help of intrusion prevention system, we can classify user activities into two categories, normal and suspicious activity. There is need to design effective intrusion prevention system by exploring deep learning for WSN. This research aims to deal with proposing algorithms and techniques for intrusion prevention system using deep packet inspection based on deep learning. In this, we have proposed deep learning model using convolutional neural network. The proposed model includes two steps, intrusion detection and intrusion prevention. The proposed model learns useful feature representations from large amount of labeled data and then classifies them. In this work, convolutional neural network is used to prevent intrusion for WSN. To evaluate and check the effectiveness of the proposed system, the wireless sensor network dataset (WSNDS) dataset is used and the tests are performed. The test results show that proposed system has an accuracy of 97% and works better than existing system. The proposed work can be used as future benchmark for the deep learning and intrusion prevention research communities.

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Section: Gray Holementioning

confidence: 99%

Intrusion prevention system using convolutional neural network for wireless sensor network

Chandre

Mahalle²,

Shinde³

2022

IJ-AI

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“…A. Wormhole using packet encapsulation [3], [4], [17] In encapsulation-based wormhole attacks, there are several nodes between two malicious nodes, and data packets are encapsulated between them. Since encapsulated data packets are sent between malicious nodes, the actual number of hops does not increase during the crawl.…”

Section: Approaches Wormhole Attack Detectionmentioning

confidence: 99%

“…B. A wormhole using a high quality / out-of-band channel [3], [4], [17] In this mode, a wormhole attack is launched using high-quality single-span out-of-band communication (called a tunnel) between malicious nodes. This tunnel can be achieved, for example, using a straight wired communication line or a long-range directional wireless communication line.…”

Section: Approaches Wormhole Attack Detectionmentioning

confidence: 99%

“…C. Wormhole utilizing high power transmission capabilities [3], [4], [17] In this type of wormhole attack, there is only one malicious node in the network that can transmit high power, and this node can communicate with other ordinary nodes over a long distance.…”

Section: Approaches Wormhole Attack Detectionmentioning

confidence: 99%

“…E. Wormhole using protocol distortion [3], [4], [17] In this wormhole attack mode, one malicious node tries to attract network traffic, distorting the routing protocol. Routing protocols based on the "shortest delay" instead of the "least hop" are at risk of wormhole attacks using protocol distortion.…”

Section: Approaches Wormhole Attack Detectionmentioning

confidence: 99%

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Detecting Wormhole Attacks in Wireless Sensor Networks

T.K.¹

2021

KAZATU

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Wireless sensor networks (WSN), consisting of wireless sensors and control devices and methods of self-organization using intelligent algorithms, demonstrate broad prospects. WSNs are subject to numerous types of threats and attacks. One of which is a wormhole attack. This article provides an overview of wormhole attack detection methods in WSN, as well as a new optimized method for detecting this attack. The detection method is based on the removal of the edges of the wormhole and causes significant changes in the length of the shortest paths between network nodes.The accuracy of the proposed algorithm is not affected by the number of wormholes

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Detection of unique delete attack in wireless sensor network using gradient thresholding‐long short‐term memory algorithm

Selvi

Shen

2022

Concurrency and Computation

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Wireless sensor network (WSN) is widely used in many fields, including agriculture, military, healthcare, monitoring, and surveillance. WSN application increases day by day due to growth in embedded and internet technologies. However, data security in WSN is challenging, and different types of attacks are increasing. Traditional intrusion detection methods analyze and identify attacks such as Denial of Service, anomaly detection, wormhole, sybil, and blackhole. Traditional methods never addressed the application layer's novel attack, such as the delete attack. Unique DELETE attack (DA) forms in the application layer and targets web servers and applications. Since unique DA use uniform resource locator requests, distinguishing them from legitimate traffic becomes difficult. The unique DA classify as unique (or) single, multiple, and repeated unique DELETE attacks; for detecting the unique DA types, gradient thresholding-long short-term memory (GT-LSTM) algorithm is proposed using packet per second and traffic rate data in WSN nodes. GT-LSTM algorithm detects unique DA through different thresholding at the training option layer in LSTM. Different gradient thresholding values in LSTM reduce the exploding gradients which fail to detect the unique DA nodes. Gradient thresholding in LSTM layers lies 1 to 3 for detecting unique DELETE attack nodes. The proposed method reduces detection time, improves accuracy, and identifies the hidden node that performs a unique DA in WSN. The unique DELETE Attack identification and performance through the proposed GT-LSTM is analyzed in the NS2 simulation environment and the test bed of WSN. Forms the experimental and simulation results, GT-LSTM performs better than fuzzy, KNN, and linear regression-based intruder detection systems. The proposed algorithm achieves 99% accuracy in detecting the DELETE attack nodes in WSN.

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Survey of wormhole attack in wireless sensor networks

Cited by 13 publications

References 31 publications

Intrusion prevention system using convolutional neural network for wireless sensor network

Intrusion prevention system using convolutional neural network for wireless sensor network

Detecting Wormhole Attacks in Wireless Sensor Networks

Detection of unique delete attack in wireless sensor network using gradient thresholding‐long short‐term memory algorithm

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