A Credit-based Method to Selfish Node Detection in Mobile Ad-hoc Network

Nobahary, S.; Babaie, Shahram

doi:10.2478/acss-2018-0015

Cited by 14 publications

(20 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The presented approach combines a packet formulation model and its trading approach. Similar work has been found in the studies of [22] [23], where credit-based intrusion detection modeling formulation is mostly considered.…”

Section: Related Worksupporting

confidence: 63%

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

et al. 2020

View full text Add to dashboard Cite

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.

show abstract

Section: Related Worksupporting

confidence: 63%

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

et al. 2020

View full text Add to dashboard Cite

show abstract

“…It also lacks energy efficiency, but the throughput performance is found entirely satisfactory. The authors in [20]- [22] also directed their research in a similar direction. The credit-based incentive mechanism refers to a policy where the node gets encouragement to route the data packets at a specific cost attribute.…”

Section: Global Reputation Systemmentioning

confidence: 96%

A Game Theory-Based Strategic Approach to Ensure Reliable Data Transmission With Optimized Network Operations in Futuristic Mobile Adhoc Networks

et al. 2020

View full text Add to dashboard Cite

The Mobile Ad-Hoc Network (MANET) incorporates a collaborative networking scenario, where dynamic host movement results in frequent topology changes. In MANET, nodes cooperate during route establishment, and the data packet must travel from source to destination through multi-hop intermediate links. The nodes in a MANET can be localized in a restricted zone, where manual intervention to setup fixed infrastructural support is practically infeasible. However, cooperative packet forwarding and data transmission is quite a common scenario in the context of MANET. Still, due to dynamic topological changes, weak, intermittent links appear within one-hop communication. This leads to a higher possibility of packet drop events and also increases the retransmission scenario, which affects the energy performance of the network. Addressing this issue, the study models a novel and intelligent packet forwarding approach based on the game theory, where trust evaluation in terms of node reputation factor also plays a very vital role. The approach also enforces an incentive modelling to stimulate the cooperation between mobile nodes during the MANET routing scenario. The system is designed and developed with evolutionary game perspectives to meet the Quality of Services (QoS) requirements. The experimental analysis supports the proposed modelling design aspects. Also, it exhibits that the reputation and trust-based game increases the utility of packet-forwarding strategy with high throughput and negligible network overhead.

show abstract

“…The approach is proposed to detect the selfish nodes in the network using Nuglets [16]. It is the combination of a packet purse model (PPM) and a packet trade model (PTM) by the credit-based approaches [17,18]. A credit-based approach called SPRIT (s simple, cheat-proof, credit-based system) is presented to stimulate the nodes to cooperate with other nodes [19].…”

Section: Related Workmentioning

confidence: 99%

“…The other group of strategies discover the selfish and malicious nodes in the credit-based methods in which the nodes should pay the cost to send the data packets and/or the nodes trade the data packet and sell it at a higher price when they have purchased a packet. Collision attack, lack of punishment, and incentives are the disadvantages of these methods [16][17][18][19][20][21][22] ( Table 1). The acknowledgment-based methods guarantee to send a packet of a node by using an acknowledgment message.…”

Section: Introductionmentioning

confidence: 99%

Selfish node detection based on hierarchical game theory in IoT

Nobahary

Garakani

Khademzadeh

et al. 2019

J Wireless Com Network

View full text Add to dashboard Cite

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%.

show abstract

A Credit-based Method to Selfish Node Detection in Mobile Ad-hoc Network

Cited by 14 publications

References 29 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)

A Game Theory-Based Strategic Approach to Ensure Reliable Data Transmission With Optimized Network Operations in Futuristic Mobile Adhoc Networks

Selfish node detection based on hierarchical game theory in IoT

Contact Info

Product

Resources

About