Lightweight trust model for the detection of concealed malicious nodes in sparse wireless ad hoc networks

Rikli, Nasser-Eddine; Alnasser, Aljawharah

doi:10.1177/1550147716657246

Cited by 17 publications

(8 citation statements)

References 32 publications

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“…Rikli et al [33] proposed a trust-profiling based mechanism that detects cloned nodes attack in wireless sensor networks. In this method, trust values called threshold values are determined for the subsequent adjacent nodes, and then these values are used to decide whether the observed values are less than the calculated threshold values.…”

Section: Related Workmentioning

confidence: 99%

A Context-Aware Information-Based Clone Node Attack Detection Scheme in Internet of Things

Hameed¹,

Garg²,

Amin³

et al. 2021

Preprint

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The rapidly expanding nature of the Internet of Things (IoT) networks is beginning to attract interest across a range of applications, including smart homes, smart transportation, smart health, and industrial contexts such as smart robotics. This cutting-edge technology enables individuals to track and control their integrated environment in real-time and remotely via a thousand IoT devices comprised of sensors and actuators that actively participate in sensing, processing, storing, and sharing information. Nonetheless, IoT devices are frequently deployed in hostile environments, wherein adversaries attempt to capture and breach them in order to seize control of the entire network. One such example of potentially malicious behaviour is the cloning of IoT devices, in which an attacker can physically capture the devices, obtain some sensitive information, duplicate the devices, and intelligently deploy them in desired locations to conduct various insider attacks. A device cloning attack on IoT networks is a significant security concern since it allows for selective forwarding, sink-hole, black-hole, and warm-hole attacks. To address this issue, this paper provides an efficient scheme for detecting clone node attack on IoT networks that makes use of semantic information about IoT devices known as context information sensed from the deployed environment to locate them securely. We design the location proof mechanism by combining location proofs and batch verification of the extended elliptic curve digital signature technique (ECDSA*) to accelerate the verification process at selected trusted nodes. We demonstrate the security of our proposed scheme and its resilience to secure clone node attack detection by conducting a comprehensive security analysis of our proposed scheme. The performance analysis and experimental results compared with existing studies suggest that our proposed scheme provides a high degree of detection accuracy with minimal detection time and significantly reduces the computation,

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Section: Related Workmentioning

confidence: 99%

A Context-Aware Information-Based Clone Node Attack Detection Scheme in Internet of Things

Hameed¹,

Garg²,

Amin³

et al. 2021

Preprint

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“…This TS-MNDS was determined to improve the rate of detection with reduced energy cost for facilitating a superior increase in the lifetime of the network. Finally, a Trust-based Lightweight Selfish Node Detection Scheme (TLSNDS) was proposed for identifying the concealed characteristics of malicious sensor nodes in the network (Rikli and Alnasser 2016). This TLSNDS was inferred to be potential in handling the issues that emerge due to the influence of selfish and jamming attack.…”

Section: Related Workmentioning

confidence: 99%

Semi Markov process inspired selfish aware co-operative scheme for wireless sensor networks (SMPISCS)

Devi

Ganesan

2019

Cybersecur

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In Wireless Sensor Network (WSN), energy and packet forwarding tendencies of sensor nodes plays a potential role in ensuring a maximum degree of cooperation under data delivery. This quantified level of cooperation signifies the performance of the network in terms of increased throughput, packet delivery rate and decreased delay depending on the data being aggregated and level of control overhead. The performance of a sensor network is highly inclined by the selfish behaving nature of sensor nodes that gets revealed when the residual energy ranges below a bearable level of activeness in packet forwarding. The selfish sensor node needs to be identified in future through reliable forecasting mechanism for improving the lifetime and packet delivery rate. Semi Markov Process Inspired Selfish aware Cooperative Scheme (SMPISCS) is propounded for making an attempt to mitigate selfish nodes for prolonging the lifetime of the network and balancing energy consumptions of the network. SMPISCS model provides a kind of sensor node's behavior for quantifying and future forecasting the probability with which the node could turn into selfish. Simulation experiments are carried out through Network Simulator 2 and the performance are analyzed based on varying the number of selfish sensor nodes, number of sensor nodes and range of detection threshold.

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“…5 The mobile nodes that are responsible for packet transfer have the possibility of joining or leaving the network without any constraints generally resulting in an unpredictable network structure. 6 Node cooperation in MANETs with the assumption of trustworthiness is essential due to the absence of centralized administration. The assumptions of genuineness and trustworthiness are practically violated due to the malicious intent and selfish behavior of mobile nodes.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy preference ranking organization method for enrichment evaluation‐based node cooperation technique for improving QoS in MANETs

Mylsamy¹,

Jayaprakash²

2022

Int J Communication

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Summary Cooperation between mobile nodes is the predominant factor required for achieving reliable data dissemination in mobile ad hoc networks (MANETs), since non‐cooperative nodes crumbles the routing process and degrades network performance. The degree of non‐cooperation rendered by mobile nodes need to be accurately identified by exploring feasible and comprehensive number of criteria to achieve maximized cooperation in uncertain MANET. In this paper, fuzzy preference ranking organization method for enrichment evaluation (FPROMETHEE)‐based node cooperation technique is proposed with the merits of trapezoidal fuzzy interval numbers that aids in the selection of high trustworthy mobile nodes for attaining maximized data delivery. This FPROMETHEE technique utilizes complex and multiple criteria for potential ranking in the presence of finite number of mobile nodes. It prevented the bias of information that is more common during the judgmental process associated with the cooperation estimation of mobile nodes. It includes information about the relative weights of criteria and decision maker's preferences for estimating the trust of mobile nodes to include or isolate them from the routing path. It is also useful in handling the degree of uncertainly involved in the process of routing without compromising Quality of Service (QoS) level in the network. The simulation experiments of the proposed FPROMETHEE technique conducted using ns‐2 simulator confirmed its predominance in enhancing the packet dissemination rate, throughput, energy consumptions, total overhead, and packet loss rate for different number of mobile nodes, malicious nodes, and pairs of source and destination nodes.

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Lightweight trust model for the detection of concealed malicious nodes in sparse wireless ad hoc networks

Cited by 17 publications

References 32 publications

A Context-Aware Information-Based Clone Node Attack Detection Scheme in Internet of Things

A Context-Aware Information-Based Clone Node Attack Detection Scheme in Internet of Things

Semi Markov process inspired selfish aware co-operative scheme for wireless sensor networks (SMPISCS)

Fuzzy preference ranking organization method for enrichment evaluation‐based node cooperation technique for improving QoS in MANETs

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