Trust evaluation model with entropy-based weight assignment for malicious node’s detection in wireless sensor networks

Yin, Xueqiang; Li, Shining

doi:10.1186/s13638-019-1524-z

Cited by 27 publications

(8 citation statements)

References 33 publications

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“…A trust evaluation model based on an entropy weight assignment method is proposed in Yin & Li (2019) for the detection of malicious nodes in WSNs. The work addresses attack types related to packet dropping or packet modification by incorporating trust indicators and estimates trust values that reflect adjacent nodes behavioral information.…”

Section: Related Workmentioning

confidence: 99%

A multiplayer game model to detect insiders in wireless sensor networks

Kantzavelou¹,

Μαγλαράς²,

Tzikopoulos³

et al. 2022

PeerJ Computer Science

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Insiders might have incentives and objectives opposed to those of the belonging organization. It is hard to detect them because of their privileges that partially protect them. In Wireless Sensor Networks (WSNs), significant security issues arise, including compromised nodes by insiders that disrupt the normal network operation. Immediate defensive actions to isolate malicious nodes would mitigate any related impacts. A multiplayer game model is proposed as a solution to the problem of insider attacks in WSNs, the Game of Wireless Sensor Networks (GoWiSeN). It is an imperfect information game, formulated with the use of non-cooperative game theory, holding the assumption that all players are rational. The model consists of several Local Intrusion Detection Systems (LIDSs), which are located to different nodes and communicate with a Global Intrusion Detection System (GIDS). Each LIDS gives suggestions whether the monitoring node is trusted or not. The game is being played between a potential attacker, the nodes and the GIDS. The GIDS is responsible for making a final decision and for isolating a compromised node in case of an internal attack. The theoretical model represents these interactions in an extensive form game. The formal elements of the game are specified, the outcomes of the game are quantified by first specifying players’ preferences, and then, by using the von Neumann-Morgenstern utility function, and payoffs are obtained. The game is constructed and solved, by locating NE in pure and mixed strategies. Experimental evaluations conducted on real network datasets, using IDSs of different capabilities, simulate special cases and compromised nodes in a WSN, verify the model efficiency, and show how the game should be played.

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Section: Related Workmentioning

confidence: 99%

A multiplayer game model to detect insiders in wireless sensor networks

Kantzavelou¹,

Μαγλαράς²,

Tzikopoulos³

et al. 2022

PeerJ Computer Science

View full text Add to dashboard Cite

show abstract

“…On the other hand, negative and zero value of preference function demonstrates the indifference between the decision makers visualized over the alternative pairs with respect to each criterion. In this context, a preference function selected by a decision maker resembles the form presented in Equation (5).…”

Section: Application Of M-polar Fuzzy Promethee For Estimating Cooper...mentioning

confidence: 99%

“…4 The cooperation mechanism among the mobile nodes is initiated for reliable data packets transfer in MANETs, since it does not possess a centralized controller for determining the routing path. 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.…”

Section: Introductionmentioning

confidence: 99%

“…The wireless nodes' mobility is determined to vary dynamically within the network, and hence, their behavior is highly time variant in nature 4 . The cooperation mechanism among the mobile nodes is initiated for reliable data packets transfer in MANETs, since it does not possess a centralized controller for determining the routing path 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 .…”

Section: Introductionmentioning

confidence: 99%

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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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“…Several studies have been devoted to optimization of trust computation based on different methods and theories such as game theory [17], matrix theory [14], beta distribution [16], weighting [13], and Bayesian statistics [15]. However, it is worth mentioning that all attempts of the aforementioned studies results in increasing the energy consumption and network complexity.…”

Section: Introductionmentioning

confidence: 99%

A Reliable Lightweight Trust Evaluation Scheme for IoT Security

Aldawsari¹,

Artoli²

2021

IJACSA

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The rapid development of smart devices and the consequent demand their reliability have posed many challenges limiting their versatility. One of the most significant challenges is safeguarding the widespread network of sensors and devices within harsh remote environments. Numerous trust schemes have been proposed to overcome related IoT security concerns. However, most of these schemes are not lightweight and consequently are not energy-efficient. This paper proposes a reliable lightweight trust evaluation scheme (RTE) to mitigate the malicious behavior of the nodes within IoT networks. The nodes are grouped into a set of clusters each having a cluster head while cluster members are categorized by evaluating their associated residual energy. Nodes with residual energy lower than the threshold (which is determined by the base station) are suspended until they recover and regain their activity. The computations are handled by the CH which is elected by an algorithm according to its energy and coverage degree in order to optimize the energy consumption in the network. For validation and performance evaluation, the proposed RTE scheme was compared to three of the recent schemes in its category. The obtained results have revealed that the proposed RTE scheme outperforms all of them in terms of detection rate, trust evaluation time, and energy efficiency.

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Trust evaluation model with entropy-based weight assignment for malicious node’s detection in wireless sensor networks

Cited by 27 publications

References 33 publications

A multiplayer game model to detect insiders in wireless sensor networks

A multiplayer game model to detect insiders in wireless sensor networks

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

A Reliable Lightweight Trust Evaluation Scheme for IoT Security

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