A secure positioning algorithm against Sybil attack in wireless sensor networks based on number allocating

Tang, Qing; Wang, Jian

doi:10.1109/icct.2017.8359771

Cited by 8 publications

(10 citation statements)

References 6 publications

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“…Another drawback of the proposed algorithm is its assumption that an adversary does not employ external powerful devices to lunch Sybil attacks. This limitation exists in many state-of-the-art algorithms like [6,9,10,11,15,[17][18][19]. A possible solution for overcoming this limitation is to utilise time-dependent client puzzles which do not permit powerful devices to solve puzzles faster than usual cases.…”

Section: Further Discussionmentioning

confidence: 99%

“…This expert system relies on an ultrawideband (UWB) ranging-based detection algorithm that operates in a distributed manner requiring no cooperation or information sharing between the nodes. Tang and Wang [11] proposed a positioning algorithm based on number allocating and mutual guarantee relying on neighbours to detect Sybil nodes in WSNs. The algorithm employs a declaration number and the guaranteed node to mark Sybil nodes in the network.…”

Section: Algorithms For Stationary Wsnsmentioning

confidence: 99%

“…Furthermore, as stated in [6], in the attack model, it is assumed that the network is insecure and that the nodes could be captured with a certain probability. Also, our attack model assumes that the adversary does not employ external powerful devices to lunch Sybil attacks, as it is assumed in many existing papers [6,9,10,11,15,[17][18][19]. Like [6,9,10,11,15,[17][18][19], it is assumed that M (M < N) legitimate nodes of the network can be captured by an adversary and reprogrammed to establish a Sybil attack.…”

Section: Attack Modelmentioning

confidence: 99%

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Detecting Sybil nodes in stationary wireless sensor networks using learning automaton and client puzzles

Jamshidi

Esnaashari

Darwesh³

et al. 2019

IET Communications

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Section: Further Discussionmentioning

confidence: 99%

Section: Algorithms For Stationary Wsnsmentioning

confidence: 99%

Section: Attack Modelmentioning

confidence: 99%

See 1 more Smart Citation

Detecting Sybil nodes in stationary wireless sensor networks using learning automaton and client puzzles

Jamshidi

Esnaashari

Darwesh³

et al. 2019

IET Communications

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“…Authors in [7], proposed another totally distributed secure positioning algorithm based on an allocating number verification and on the mutual guarantee relying on neighbours for Wireless Sensor Networks (WSN). This algorithm uses a lot of messages in order to guarantee the correct location of nodes, which increases the cost of such algorithm.…”

Section: Authors In [4] Proposed a Rssi-based Localization Technique mentioning

confidence: 99%

A Macroscopic Traffic Model-based Approach for Sybil Attack Detection in VANETs

et al. 2019

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Vehicular ad hoc networks (VANETs) are expected to play an important role in our lives. They will improve traffic safety and bring a revolution on the driving experience. However, these benefits are counterbalanced by possible attacks that threaten not only the vehicle's security, but also passengers lives. One of the most common ones is the Sybil attack, which is more dangerous than others since it could be the starting point of many other attacks in VANETs. This paper proposes a distributed approach allowing the detection of Sybil attacks using the traffic flow theory. The key idea here is that each vehicle will monitor its neighbourhood in order to detect an eventual Sybil attack. This is achieved by comparing between the real accurate speed of the vehicle and the one estimated using the V2V communications with vehicles in the vicinity. This estimated speed is obtained using the traffic flow fundamental diagram of the road's portion where the vehicles are moving. A mathematical model that evaluates the rate of Sybil attack detection according to the traffic density is proposed. Then, this model is validated through some extensive simulations conducted using the well-known NS3 network simulator together with SUMO traffic simulator.

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“…If there is a new node in the network, the system will identify the node and provide a digital signature to the node by stating that the node is authentic [8]. An algorithm was proposed by Tang and Wang [9] to determine the position or the coordinate of every node in the VANET so that the Sybil attack will be detected conveniently. In addition, Tang proposed the existence of identity (ID) for each node to identify whether the node had previously carried out a Sybil attack or the ID had been forged in a previous Sybil attack.…”

Section: Introductionmentioning

confidence: 99%

Sybil Attack Prediction on Vehicle Network Using Deep Learning

Helmi

Adriman

Arif

et al. 2022

J. RESTI (Rekayasa Sist. Teknol. Inf.)

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Vehicular Ad Hoc Network (VANET) or vehicle network is a technology developed for autonomous vehicles in Intelligent Transportation Systems (ITS). The communication system of VANET is using a wireless network that is potentially being attacked. The Sybil attack is one of the attacks that occur by broadcasting spurious information to the nodes in the network and could cause a crippled network. The Sybil strikes the network by camouflaging themselves as a node and providing false information to nearby nodes. This study is conducted to predict the Sybil attack by analyzing the attack pattern using a deep learning algorithm. The variables exerted in this research are time, location, and traffic density. By implementing a deep learning algorithm enacting the Sybil attack pattern and combining several variables, such as time, position, and traffic density, it reaches 94% of detected Sybil attacks.

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A secure positioning algorithm against Sybil attack in wireless sensor networks based on number allocating

Cited by 8 publications

References 6 publications

Detecting Sybil nodes in stationary wireless sensor networks using learning automaton and client puzzles

Detecting Sybil nodes in stationary wireless sensor networks using learning automaton and client puzzles

A Macroscopic Traffic Model-based Approach for Sybil Attack Detection in VANETs

Sybil Attack Prediction on Vehicle Network Using Deep Learning

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