A simple, lightweight, and precise algorithm to defend against replica node attacks in mobile wireless networks using neighboring information

Jamshidi, Mojtaba; Poor, Shokooh Sheikh Abooli; Arghavani, Abbas; Esnaashari, Mehdi; Shaltooki, Abdusalam Abdulla; Meybodi, Mohammad Reza

doi:10.1016/j.adhoc.2020.102081

Cited by 8 publications

(14 citation statements)

References 25 publications

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“…Simulation outcome of three different methods such as KRAD-PRLCSSR technique, Lightweight replica node detection mechanism [1], EMA model [2] are discussed with various metrics. These metrics are described as given below.…”

Section: Resultsmentioning

confidence: 99%

“…Performance of KRAD-PRLCSSR technique, Lightweight replica node detection mechanism [1], EMA model [2] is implemented in NS3. 500 sensor nodes are arbitrarily deployed over a squared area of 𝐴 2 (1100 m * 1100 m).…”

Section: Simulation Settingsmentioning

confidence: 99%

“…Table II demonstrates the performance of attack detection accuracy using three different methods namely KRAD-PRLCSSR technique, Lightweight replica node detection mechanism [1], EMA model [2]. As shown in Table II, ten runs are carried out for each method with different counts of input 50,100,150,200,…500.…”

Section: Public Keymentioning

confidence: 99%

“…A lightweight replica node detection mechanism was developed in [1] using three-step process to discover the replica nodes with lesser false detection probability. However, the higher security of data delivery was not achieved.…”

Section: Introductionmentioning

confidence: 99%

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Kriging Regressive Pseudo Randomized Lamport Certificateless Signcryption based Replication Attack Detection for Secure Routing in WSN

D.Sudhakar¹

2021

TURCOMAT

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A Wireless Sensor Network (WSN) includes small, low-cost, and resource-constrained sensor nodes to perform monitoring. Each node is free to move and susceptible to various attacks. Therefore, WSNs have huge attention to communication security. Identifying and monitoring attack node is complex in distributed network and creates node replication attacks in WSN. In order to improve secure routing, a novel technique called Kriging Regressive Attack Detection based Pseudo Randomized Lamport Certificateless Signcryptive Secure Routing (KRAD-PRLCSSR) is proposed. KRAD-PRLCSSR enhances the secure routing by detecting the replication attack in WSN. Initially, the neighbor discovery is performed using the Kriging Regression function. Energy of every sensor node is calculated and validates with threshold value. Based on estimated results, normal node or replica node is identified. Then the normal node is chosen as neighboring node. After that, secure transmission is performed using Pseudo Randomized Lamport one-time Generative Certificateless Signcryption. By applying the signcryption technique, Pseudo Randomized private and public keys are generated for each sensor node. After the key generation, the source node performs the encryption as well as a digital signature. The encrypted data and signature are sent to receiver. Finally, signature is verified at the receiver side to decrypt the data. This helps to increase the security of routing in WSN. The simulation analysis of KRAD-PRLCSSR technique is performed with the different metrics.

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Section: Resultsmentioning

confidence: 99%

Section: Simulation Settingsmentioning

confidence: 99%

Section: Public Keymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Kriging Regressive Pseudo Randomized Lamport Certificateless Signcryption based Replication Attack Detection for Secure Routing in WSN

D.Sudhakar¹

2021

TURCOMAT

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“…Jamshidi et al suggested one more clone node detection algorithm in [43], which uses the mobility model. e sensor node will meet with the same node in each monitoring round.…”

Section: Related Workmentioning

confidence: 99%

An Efficient Three-Phase Fuzzy Logic Clone Node Detection Model

Lalar

Bhushan

Jangra

et al. 2021

Security and Communication Networks

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Wireless sensor networks have been deployed in the open and unattended environment where the attacker can capture the sensors and create the replica of captured nodes. As the clone nodes have been considered legitimate nodes, clone nodes can initiate different network attacks. We have designed a three-phase clone node detection method named fuzzy logic clone node detection (FLCND). The first phase of FLCND checks whether any node is missing from the network or not. In the next phase, FLCND finds out whether any missing node has arisen in the network in a stipulated time. If any missing node is alive, there is a possibility the node may be cloned. The information of suspected nodes is entered into the Hot-List, which has been maintained in the network. Phase III uses the suspected list and finds out the possibility of clone node using fuzzy logic. Two different scenarios have been simulated in NS2 to evaluate FLCND. The simulation result shows that the proposed method increases the packet delivery ratio (PDR) and reduces packet loss, end-to-end delay, and energy consumption. The simulation results illustrate that the FLCND method reduces the average power consumption by 27% and increases the detection rate by 46% compared to the existing techniques.

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On Optimal Configuration of IDS for RPL Resource-Constrained Networks Using Evolutionary Algorithm

Alshahrani

Clark

2022

Proceedings of the Future Technologies Conference (FTC) 2022, Volume 2

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A simple, lightweight, and precise algorithm to defend against replica node attacks in mobile wireless networks using neighboring information

Cited by 8 publications

References 25 publications

Kriging Regressive Pseudo Randomized Lamport Certificateless Signcryption based Replication Attack Detection for Secure Routing in WSN

Kriging Regressive Pseudo Randomized Lamport Certificateless Signcryption based Replication Attack Detection for Secure Routing in WSN

An Efficient Three-Phase Fuzzy Logic Clone Node Detection Model

On Optimal Configuration of IDS for RPL Resource-Constrained Networks Using Evolutionary Algorithm

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