An integrated game theoretical approach to detect misbehaving nodes in MANETs

Vijayakumaran, C.; Macriga, T. Adiline

doi:10.1109/iccct2.2017.7972268

Cited by 8 publications

(13 citation statements)

References 12 publications

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“…A controlled identical mechanism of game and also another game theory modeling to detect and isolate malicious nodes are evaluated by the studies of [29] and [30], respectively. Both the studies concluded their research scope into futuristic dynamic IoT networks.…”

Section: Related Workmentioning

confidence: 99%

A Novel Multi-Agent and Multilayered Game Formulation for Intrusion Detection in Internet of Things (IoT)

et al. 2020

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The current era of smart computing and enabling technologies encompasses the Internet of Things (IoT) as a network of connected, intelligent objects where objects range from sensors to smartphones and wearables. Here, nodes or objects cooperate during communication scenarios to accomplish effective throughput performance. Despite the deployment of large-scale infrastructure-based communications with faster access technologies, IoT communication layers can still be affected with security vulnerabilities if nodes/objects do not cooperate and intend to take advantage of other nodes for fulfilling their malevolent interest. Therefore, it is essential to formulate an intrusion detection/prevention system that can effectively identify the malicious node and restrict it from further communication activities-thus, the throughput, and energy performance can be maximized to a significant extent. This study introduces a combined multi-agent and multilayered game formulation where it incorporates a trust model to assess each node/object, which is participating in IoT communications from a security perspective. The experimental test scenarios are numerically evaluated, where it is observed that the proposed approach attains significantly improves intrusion detection accuracy, delay, and throughput performance as compared to the existing baseline approaches.

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Section: Related Workmentioning

confidence: 99%

A Novel Multi-Agent and Multilayered Game Formulation for Intrusion Detection in Internet of Things (IoT)

et al. 2020

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“…The routing task confirmation step is done when the source node is routed to the destination node by using the DSR method. A new routing task is assigned by the middle relay node to the new node, and this assignment confirmation should be created for it, which is assumed by the hash function as a signature function by the supervisor in the verification phase [20]. The proposed mechanism is a multi-phase method based on reputation and game theory for stimulation of cooperation between selfish nodes in the internet of things, and this mechanism has been designed in three phases including setup and clustering, sending data and playing a multi-person game, and update and detecting selfish and malicious nodes.…”

Section: -Related Workmentioning

confidence: 99%

“…In the game theory methods, each node plays a role as players in the game, which are interacting with each other and design the game and its profits to send the data packets. These methods have a higher false positive rate, and the average end to end delay is more than other methods [19][20][21]. Another group of selfish nodes detection and recognition methods in the network is hybrid and specified methods.…”

Section: -Introductionmentioning

confidence: 99%

Selfish Node detection based on GA and Learning Automata in IoT

Nobahary¹,

Garakani²,

Khademzadeh³

et al. 2020

Preprint

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It is critical to increasing the network throughput on the internet of things with short-range nodes. Nodes prevent to cooperate with other nodes in the network are known as selfish nodes. Previous studies have done on the selfish nodes detection that leads to increase throughput and reduce the end to end delay. The proposed method for discovering the selfish node is based on genetic algorithm and learning automata. It consists of three phases of setup and clustering, the best routing selection based on genetic algorithm, and finally, the learning and update phase. For appropriate network performance, the clustering algorithm implemented in the first phase. Nodes are working together to send the data packet to the destination in the second phase, and the neighbor node selected for forwarding the data packet in which that node has a high value of fitness function, among others. In the third phase, each node monitors the performance of its neighbor nodes in forwarding the data packet and uses the learning automata system to identify the selfish nodes. By preventing to cooperate selfish nodes and decreasing the probability selection of selfish nodes, it increases the throughput in the network. The results of the simulation show that the detection accuracy of selfish nodes in comparison with the existing methods average 12%, and the false positive rate has decreased by 5%.

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“…The payoff of wireless stations is based on the individual power of each station. Vijayakumaran et al proposed a novel detection of the selfish node, which consists of two phases [31]. The generation phase includes routing task confirmation phase and the routing-report generation phase, and coordination-confirmation report generation phase.…”

Section: Related Workmentioning

confidence: 99%

“…Game theorybased approaches take advantage of the incentive mechanisms through payoffs and have lower false-positive rate and overhead (time, supervisory, hardware) compared to the other aforementioned methods in the network to detect the selfish and malicious nodes. However, the prior game theory-based methods have less throughput and more end-to-end delay, and the proposed methods attempt to increase these parameters [28][29][30][31].…”

Section: Introductionmentioning

confidence: 99%

Selfish node detection based on hierarchical game theory in IoT

Nobahary

Garakani

Khademzadeh

et al. 2019

J Wireless Com Network

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Cooperation between nodes is an effective technology for network throughput in the Internet of Things. The nodes that do not cooperate with other nodes in the network are called selfish and malicious nodes. Selfish nodes use the facilities of other nodes of the network for raising their interests. But malicious nodes tend to damage the facilities of the network and abuse it. According to reviews of the previous studies, in this paper, a mechanism is proposed for detecting the selfish and malicious nodes based on reputation and game theory. The proposed method includes three phases of setup and clustering, sending data and playing the multi-person game, and update and detecting the selfish and malicious nodes. The process of setup and clustering algorithm are run in the first phase. In the second phase, the nodes of each cluster cooperate with each other in order to execute an infinite repeated game while forwarding their own or neighbor nodes' data packets. In the third phase, each node monitors the operation of its neighbor nodes for sending the data packets, and the process of cooperation is analyzed for determining the selfish or malicious nodes which forwarded the data packets with delay or even not sent them. The other nodes reduce the reputation of the nodes which does not cooperate with them, and they do not cooperate with the selfish and malicious nodes, as punishment. So, selfish and malicious nodes are stimulated to cooperate. The results of simulation suggest that the detection accuracy of the selfish and malicious nodes has been increased by an average of 12% compared with the existing methods, and the false-positive rate has been decreased by 8%.

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An integrated game theoretical approach to detect misbehaving nodes in MANETs

Cited by 8 publications

References 12 publications

A Novel Multi-Agent and Multilayered Game Formulation for Intrusion Detection in Internet of Things (IoT)

A Novel Multi-Agent and Multilayered Game Formulation for Intrusion Detection in Internet of Things (IoT)

Selfish Node detection based on GA and Learning Automata in IoT

Selfish node detection based on hierarchical game theory in IoT

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