Dynamic Trust Management for Delay Tolerant Networks and Its Application to Secure Routing

Chen, Ing-Ray; Bao, Fenye; Chang, Moonjeong; Cho, Jin-Hee

doi:10.1109/tpds.2013.116

Cited by 173 publications

(72 citation statements)

References 19 publications

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“…[4] Scholars have put forward an trust management strategy applied to the secure routing. Comparative analysis was used to set the running environment of trust models and set parameters, maximizing routing performance and reduce trust bias.…”

Section: IImentioning

confidence: 99%

“…A part of trust value need to be obtained from other nodes, that is recommendation trust. Total confidence value formulas shows in (4): (4) Where T is the total value of trust, i and j are nodes in opportunistic network, where i represents the evaluating node, j represents the node being evaluated. Ti, j is the total…”

Section: B Trust Property Integration Mechanismmentioning

confidence: 99%

“…Absolute differences of every point on the curve at series of comparison sequence Xi and each point on the curve of reference series X0 denoted as Oi (k) [4]. 4) Relevancy degree.…”

Section: B Trust Property Integration Mechanismmentioning

confidence: 99%

“…4) Relevancy degree. Correlation coefficients are the correlation value of comparison sequence associated with the reference sequence in all moments, there are much value, and information too scattered are not convenient to compare integrity [4]. Therefore it is necessary to concentrate correlation coefficient to a value each time (that is, all points in the curve) Correlation degree is calculated as (8): (8) To calculate 1, 2, 3 according to the correlation degree as(9): (9) Grey correlation reflects the influence of various factors affect the performance to the network trust model, it assigned the proper weight coefficient for the direct trust value distribution in the model, optimized the model performance.…”

Section: B Trust Property Integration Mechanismmentioning

confidence: 99%

See 3 more Smart Citations

An Trust Model Based on Grey Relational Analysis and Cooperative Computing in Opportunity Networks

Xu¹,

Shou²

2017

Proceedings of the 3rd International Conference on Wireless Communication and Sensor Networks (WCSN 2016)

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Abstract-The existence of selfish nodes and malicious nodes pose a significant threat to the complex environment of opportunistic network. In order to improve the transmission security in bad environment, a multi-dimensional trust model GRATM(Grey Relational Analysis Trust Model) based on gray relational degree and cooperative computation is proposed which may improve the success rate and reduce the delay during the process of transmission. GRATM adopts multi-dimensional parameters, the parameters are fused and updated dynamically, and the total trust value is calculated by direct interaction combine with other nodes recommendation trust value. GRATM is based on gray relational analysis, the weight coefficients of the direct trust value are reasonably distributed, and the recommendation trust value is calculated by the neighbor nodes. Trust information is sent to other nodes and exchanged with each other through regular sending package. Compared with Direct, MaxProp, and Direct Delivery, GRATM has the advantages of balanced transmission success rate, transmission delay and performance.

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Section: IImentioning

confidence: 99%

Section: B Trust Property Integration Mechanismmentioning

confidence: 99%

“…Absolute differences of every point on the curve at series of comparison sequence Xi and each point on the curve of reference series X0 denoted as Oi (k) [4]. 4) Relevancy degree.…”

Section: B Trust Property Integration Mechanismmentioning

confidence: 99%

Section: B Trust Property Integration Mechanismmentioning

confidence: 99%

See 2 more Smart Citations

An Trust Model Based on Grey Relational Analysis and Cooperative Computing in Opportunity Networks

Xu¹,

Shou²

2017

Proceedings of the 3rd International Conference on Wireless Communication and Sensor Networks (WCSN 2016)

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“…Some Authors [18], [19] have addressed packet dropping attacks in DTNs as blackhole, grayhole and routing misbehaviour of nodes. These attacks are carried out by compromised nodes with forged metrics that have the tendencies to drop all (blackhole attack) or a certain percentage (grayhole attack) of the messages to be forwarded.…”

Section: ) Packet Dropping Attacks In Dtnsmentioning

confidence: 99%

Analysis of DoS Attacks in Delay Tolerant Networks for Emergency Evacuation

Asuquo

Cruickshank

Sun

et al. 2015

2015 9th International Conference on Next Generation Mobile Applications, Services and Technologies

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Abstract-In the event of a disaster, there is a severe damage/destruction to physical infrastructures such as telecommunication and power lines which result in the disruption of communication in this areas. For such scenarios, Delay Tolerant Network (DTN) provides an alternative means of communication.In Delay Tolerant Networks (DTNs), a message from a source node may be delivered to the destination node despite the nonexistence of an infrastructure and an end-to-end connectivity. However DTNs are susceptible to security threats such as DoS attacks targeted at disrupting relayed packets or dropping critical packets during a disaster rescue operation. DoS attacks consist of blackhole, grayhole, wormhole, packet flooding attacks etc. The scope of this paper is to study the impacts of blackhole and packet flooding attacks in a post disaster communication network using DTN. Various performance metrics in DTN have been used to study the impacts of different DoS attacks in DTN and a comprehensive analysis is presented.

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Integrating CLDs and machine learning through hybridization for human‐centric wireless networks

Kumari,

Yadav,

Cengiz

et al. 2023

Trans Emerging Tel Tech

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Wireless sensor networks, more commonly abbreviated as WSNs, have been regarded as helpful tool for managing human‐centric applications. Nevertheless, the design of wireless systems that are accurate, efficient, and robust remains difficult due to the variables and dynamics of the wireless environment as well as the requirements of the users. Cross‐layer designs along with the machine‐learning techniques need to be integrated into a novel hybridization framework for human‐centric wireless networks in order to simplify the process and make it more manageable. The purpose of the proposed framework is to enhance wireless sensor networks (WSNs) in terms of their energy efficiency, robustness, real‐time performance, and scalability. In particular, machine learning are employed for the purpose of extracting features from sensor data, and the framework combines cross‐layer optimization and RL in order to facilitate effective and adaptable communication and networking. In comparison to previous work in this field, the accuracy, energy consumption, robustness, real‐time performance, and scalability of the proposed framework are all significantly improved. The hybridization framework that has been proposed provides a promising approach to addressing the challenges, and it can be of use to a variety of applications.

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Dynamic Trust Management for Delay Tolerant Networks and Its Application to Secure Routing

Cited by 173 publications

References 19 publications

An Trust Model Based on Grey Relational Analysis and Cooperative Computing in Opportunity Networks

An Trust Model Based on Grey Relational Analysis and Cooperative Computing in Opportunity Networks

Analysis of DoS Attacks in Delay Tolerant Networks for Emergency Evacuation

Integrating CLDs and machine learning through hybridization for human‐centric wireless networks

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