Impact of Sybil and Wormhole Attacks in Location Based Geographic Multicast Routing Protocol for Wireless Sensor Networks

Vignesh, Ramachandran

doi:10.3844/jcssp.2011.973.979

Cited by 17 publications

(5 citation statements)

References 20 publications

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“…The wireless nodes have low computational capabilities and are highly resource constrained. Routing protocols of wireless networks are prone to various routing attack, such as black hole, rushing and Denial of Service (DoS) attacks (Ramachandran and Shanmugan, 2011). There is an improved risk of security attacks, to defeat concealed attacks there is a necessity to authenticate both access points and wireless stations (Moorthy and Sathiyabama, 2012).…”

Section: General Discussion and Related Workmentioning

confidence: 99%

Denial of Service Attack in Distributed Wireless Network by Distributed Jammer Network: A Birth-Death Random Process Analysis

Dhanasekaran¹

2014

Journal of Computer Science

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Large number of low power, tiny radio jammers are constituting a Distributed Jammer Network (DJN) is used nowadays to cause a Denial of Service (DoS) attack on a Distributed Wireless Network (DWN). Using NANO technologies, it is possible to build huge number of tiny jammers in millions, if not more. The Denial of Service (DoS) attacks in Distributed Wireless Network (DWN) using Distributed Jammer Network (DJN) considering each of them as separate Poisson Random Process. In an integrated approach, in this study, we advocate the more natural birth-death random process route to study the impact of Distributed Jammer Network (DJN) on the connectivity of Distributed Wireless Network (DWN). We express that the Distributed Jammer Network (DJN) can root a phase transition in the performance of the target network. We use Birth-Death Random Process (BDRP) route for this phase transition to evaluate the collision of Distributed Jammer Network (DJN) on the connectivity and global percolation of the target network. This study confirms the global percolation of Distributed Wireless Network (DWN) is definite when the Distributed Jammer Network (DJN) is not more significant.

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Section: General Discussion and Related Workmentioning

confidence: 99%

Denial of Service Attack in Distributed Wireless Network by Distributed Jammer Network: A Birth-Death Random Process Analysis

Dhanasekaran¹

2014

Journal of Computer Science

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“…Impact of Sybil and Wormhole Attacks in WSN was analyzed in [19] with assists of Location Based Geographic Multicast Routing Protocol. A channel-based authentication technique was designed in [20] to detect Sybil attacks in wireless networks, utilizing the uniqueness of channel responses in the rich-scattering environment.…”

Section: Related Workmentioning

confidence: 99%

Delta Ruled First Order Iterative Deep Neural Learning for Sybil and Wormhole Attacks Detection in Healthcare Wireless Sensor Network

A¹,

Sampoornam²,

R³

2021

Preprint

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Detection and isolation of Sybil and wormhole attack nodes in healthcare WSN is a significant problem to be resolved. Few research works have been designed to identify Sybil and wormhole attack nodes in the network. However, the detection performance of Sybil and wormhole attack nodes was not effectual as the false alarm rate was higher. In order to overcome such limitations, Delta Ruled First Order Iterative Deep Learning based Intrusion Detection (DRFOIDL-ID) Technique is proposed. The DRFOIDL-ID Technique includes two main phase namely attack detection and isolation. The DRFOIDL-ID Technique constructs Delta Ruled First Order Iterative Deep Learning in attack detection phase with aim of detecting the occurrence of Sybil and wormhole attacks in healthcare WSN. After detecting the attack nodes, DRFOIDL-ID Technique carried outs isolation process with the objective of increasing the routing performance. During the isolation phase, DRFOIDL-ID Technique keep always the identified Sybil and wormhole attack nodes through transmitting the isolation messages to all sensor nodes in healthcare WSN. Hence, DRFOIDL-ID Technique improves the routing performance with lower packet loss rate. The DRFOIDL-ID Technique conducts the simulation process using factors such as attack detection rate, attack detection time, false alarm rate and packet loss rate with respect to a diverse number of sensor nodes and data packets. The simulation result proves that the DRFOIDL-ID Technique is able to improve the attack detection rate and also reduces the attack detection time as compared to state-of-the-art works.

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“…Algorithms proposed in [14][15][16] use the concept of common neighbors to detect Sybil nodes. In [17], another algorithm is proposed for detecting Sybil attack to multicast routing protocols based on geographic location. In [18], a method is developed which collects routes' information using Swarm Intelligence algorithm during network operation and detects Sybil nodes through their energy changes in the course of network activity.…”

Section: A Related Workmentioning

confidence: 99%

“…Also, in [17][18][19] algorithms are proposed for detecting Sybil nodes in mobile sensor networks. In [17], a centralized…”

Section: Introductionmentioning

confidence: 99%

Sybil Node Detection in Mobile Wireless Sensor Networks Using Observer Nodes

Jamshidi

Ranjbari

Esnaashari

et al. 2018

JOIV : Int. J. Inform. Visualization

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Sybil attack is one of the well-known dangerous attacks against wireless sensor networks in which a malicious node attempts to propagate several fabricated identities. This attack significantly affects routing protocols and many network operations, including voting and data aggregation. The mobility of nodes in mobile wireless sensor networks makes it problematic to employ proposed Sybil node detection algorithms in static wireless sensor networks, including node positioning, RSSI-based, and neighbour cooperative algorithms. This paper proposes a dynamic, light-weight, and efficient algorithm to detect Sybil nodes in mobile wireless sensor networks. In the proposed algorithm, observer nodes exploit neighbouring information during different time periods to detect Sybil nodes. The proposed algorithm is implemented by J-SIM simulator and its performance is compared with other existing algorithm by conducting a set of experiments. Simulation results indicate that the proposed algorithm outperforms other existing methods regarding detection rate and false detection rate. Moreover, they also showed that the mean detection rate and false detection rate of the proposed algorithm are respectively 99% and less than 2%.

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Impact of Sybil and Wormhole Attacks in Location Based Geographic Multicast Routing Protocol for Wireless Sensor Networks

Cited by 17 publications

References 20 publications

Denial of Service Attack in Distributed Wireless Network by Distributed Jammer Network: A Birth-Death Random Process Analysis

Denial of Service Attack in Distributed Wireless Network by Distributed Jammer Network: A Birth-Death Random Process Analysis

Delta Ruled First Order Iterative Deep Neural Learning for Sybil and Wormhole Attacks Detection in Healthcare Wireless Sensor Network

Sybil Node Detection in Mobile Wireless Sensor Networks Using Observer Nodes

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