Autonomous detection of malevolent nodes using secure heterogeneous cluster protocol

Kotteeswaran, C.; Patra, Indrajit; Nagaraju, Regonda; Sungeetha, D.; Kommula, Bapayya Naidu; Algani, Yousef Methkal Abd; Murugavalli, S.; Bala, B. Kiran

doi:10.1016/j.compeleceng.2022.107902

Cited by 2 publications

(2 citation statements)

References 13 publications

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“…A similar problem is faced by Gomathy et al 9 and Pon et al 31 because that is also a cluster‐based scheme. In Reference 32, a trust‐based probabilistic malicious node detection method is proposed where a periodically accessible trusted authority has to be deployed to pass judgment on the behavior of suspicious nodes based on gathered routing evidence with self and coordinate monitoring. Authors claim that it improves network throughput by 34% compared to previous methodologies.…”

Section: Related Workmentioning

confidence: 99%

Efficient selfish node detection using SVM in IoT‐MANET environment

Ghosh,

Banerjee,

Sufian

et al. 2023

Trans Emerging Tel Tech

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The communication between nodes in the Internet of Things‐based mobile ad‐hoc networks environment is typically dependent on their selfless attitude. Whenever a node needs to send a message to another node in the network, it needs the help of one or more nodes to act as a router(s) that will bridge the gap between the source and the destination. All these selfless forwarding's require energy, bandwidth, and other resources. Therefore, some routers often raise a link breakage attack to save their resources. They keep silent after receiving a message; neither they acknowledge it nor they forward it. These selfish nodes must be identified and carefully avoided while choosing routes; otherwise, numerous messages will have to be resent, including control messages like route‐request. The identification depends on the past behavior of the node as well as velocity, the direction of movement, current geographical location and so forth. This article presents a support vector machine‐based node classification method that checks whether nodes are intentionally issuing a link breakage attack or it is really out of the radio range of its predecessor. The simulation results of the proposed method demonstrate that the proposed technique can correctly detect most of the selfish activities in the network and that also in much lesser time. It also enhances the packet delivery ratio and reduces delay and energy consumption.

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Section: Related Workmentioning

confidence: 99%

Efficient selfish node detection using SVM in IoT‐MANET environment

Ghosh,

Banerjee,

Sufian

et al. 2023

Trans Emerging Tel Tech

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“…Security is always a important factor in MANET due to frequent connection interruptions, bandwidth, resource constraint and high mobility of the wireless nodes. The nodes may behave selfishly and maliciously due to the energy constraints in forwarding other nodes' packets as they have to use their energy (1)(2)(3)(4) . Many conventional cryptography-based approaches are in existence to resolve security issues in MANET but these approaches are inefficient in isolating the untrustworthy nodes.…”

Section: Introductionmentioning

confidence: 99%

A Novel Trust Adaptability Approach for Secure Data Transmissions using Enhanced Collaborative Nodes Trustworthiness in Mobile Ad-hoc Networks

Reddy¹,

Srinivas²,

Mohan³

2022

IJST

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Objectives: To find an efficient security routing model based on trust adaptability by considering various transmission parameters that influence the node's behavior. Methods: Enhanced Collaborative Trust Based Approach (ECTBA) was applied to isolate the malicious nodes from routing by computing their enhanced collaborative trust value based on the node's behavior using transmission parameters. Parameters that influence the node's behavior like the number of data packets and control packets forwarded, dropped, or misrouted by the node are quantified to compute direct trust value and neighbor reputation. Node's Enhanced Collaborative trust value was generated by the combination of direct and neighbor observations. Findings: The proposed strategy is compared with several cases like the Direct Trust Based Approach (DTBA), where routing involves trustworthy nodes categorized based on only direct trust, existing methods like Belief-dependent trust evolution method(BETM), Novel extended trust-dependent method (NETM) where routing is done with nodes that are categorized as trustworthy depending on direct and indirect observations and simple AODV routing performed with all the possible random nodes without any trust detection. The performance parameters of the proposed ECTBA exhibit a success rate of 10.2% in false positives detection (FPD),network throughput of 438.12 Kbps,and packet delivery ratio (PDR) of 92.3%.This method proves to be a better method when compared with the traditional trust-based security methods(BETM and NETM) in terms of efficiency. Novelty: This research suggested a novel and fine-tuned method for quantifying a node's trustworthiness and for secure routing that coupled the direct and indirect observations into enhanced collaborative trust https://www.indjst.org/

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Autonomous detection of malevolent nodes using secure heterogeneous cluster protocol

Cited by 2 publications

References 13 publications

Efficient selfish node detection using SVM in IoT‐MANET environment

Efficient selfish node detection using SVM in IoT‐MANET environment

A Novel Trust Adaptability Approach for Secure Data Transmissions using Enhanced Collaborative Nodes Trustworthiness in Mobile Ad-hoc Networks

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