Trust management system for mobile cluster-based wireless sensor network

Abramov, Evgeny; Basan, Elena; Makarevich, Oleg

doi:10.1145/2799979.2800001

Cited by 3 publications

(3 citation statements)

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“…Abramov and his peers implemented TMS [6]; a trust algorithm based on their previous trust model in [12]. The trust model encompasses metrics that characterize different malicious behaviors.…”

Section: Related Workmentioning

confidence: 99%

“…Conversely, non-cryptographic techniques like reputation and anomaly detection systems are more concerned with isolating adversaries that are actively participating in the network. Current security research in WSNs [3], [4], [5], [6], [7] is geared towards this approach as the attacker is engaged throughout the process, constantly being faced with the challenge of survival in the network before eventually being isolated. Focusing on intrusion detection and isolation as opposed to prevention also minimizes the impact it has on the openness and flexibility of WSNs.…”

Section: Introductionmentioning

confidence: 99%

“…In this paper, we developed a trust management scheme "TrustSense" that leverages novel ideas from some existing implementations [7], [6] to provide a rich and energy-efficient system that achieves a better balance between resiliency and energy trade-off. It combines the use of periodic trust-updates and caching for reducing communication overhead, linear spatial variogram for outlier detection and a strict reward system to quicken the isolation of malicious nodes.…”

Section: Introductionmentioning

confidence: 99%

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TrustSense: An energy efficient trust scheme for clustered wireless sensor networks

Odesile,

Lagesse

2020

Preprint

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Designing security systems for wireless sensor networks presents a challenge due to their relatively low computational resources. This has rendered many traditional defense mechanisms based on cryptography infeasible for deployment on such networks. Reputation and anomaly detection systems have been implemented as viable alternatives, but existing implementations still struggle with providing efficient security without a significant impact on energy consumption. To address this trade-off between resource consumption and resiliency, we designed TrustSense, a reputation management protocol for clustered WSNs. It is a semi-centralized family of algorithms that combine periodic trust updates, spatial correlation, and packet sequence validation at the cluster-heads' hierarchy to relieve the sensor nodes of unnecessary opinion queries and trust evaluation computation. We compared the efficiency of TrustSense with legacy reputation systems such as EigenTrust and the results of simulations show a significant improvement in reliability and energy usage while maintaining an acceptable path length with varying numbers of malicious nodes. We believe the approach of combining different techniques from various classes of intrusion detection systems unlocks several possibilities of achieving better results by more complex and versatile composition of these techniques.

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Section: Related Workmentioning

confidence: 99%