Proof of Sense: A Novel Consensus Mechanism for Spectrum Misuse Detection

Fernando, Pramitha; Dadallage, Keshawa; Gamage, Tharindu D.; Seneviratne, Chatura; Madanayake, Arjuna; Liyanage, Madhusanka

doi:10.1109/tii.2022.3169978

Cited by 13 publications

(4 citation statements)

References 20 publications

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“…Effective spectrum management is crucial in beyond 5G networks, as discussed in [15]. A novel consensus mechanism, "Proof-of-Sense," is proposed for blockchain-based dynamic spectrum access (DSA) systems.…”

Section: Detailed Study Of Fraud Detection Methodsmentioning

confidence: 99%

Machine Learning Models for Fraud Detection: A Comprehensive Review and Empirical Analysis

L. K. Vishwamitra

2024

jes

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An in-depth familiarity with ML and DL models for fraud detection is essential due to the growing frequency and complexity of fraudulent activity across many domains. Despite the abundance of research on the subject, empirical analyses of these models, especially in their real-time implementations, are typically lacking. This study fills that need by meticulously reviewing and analysing ML and DL models developed for fraud detection. We draw attention to the shortcomings of existing approaches, which are crucial in the ever-changing field of fraud detection and include problems with recall, scalability, complexity, precision, and accuracy. By evaluating various ML and DL models using these measures, our evaluation method is based on a rigorous empirical approach. Provided that insights into the practical consequences as well as flexibility of each model in real-time circumstances, they thoroughly assess their performance. There are many ways in which this work will be useful; for example, it will help professionals choose the best models for their fraud detection needs, improve academic knowledge of these models in practice, and open the door to more studies that will concentrate on developing better fraud detection tools. This comprehensive research gives academics and companies a foundation for better, more effective and more scalable fraud detection systems in this period of essential digital security.

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Section: Detailed Study Of Fraud Detection Methodsmentioning

confidence: 99%

Machine Learning Models for Fraud Detection: A Comprehensive Review and Empirical Analysis

L. K. Vishwamitra

2024

jes

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“…The anomaly detection system is instrumental in detecting violations of spectrum access policies, detecting malicious intent, and separating good actions from bad, assigning a reputation metric to nodes. A novel consensus mechanism, "Proof-of-Sense" is proposed by [147] for a BC-based DSA system. Operating on spectrum sensing procedures rather than cryptographic calculations, this mechanism detects spectrum fraud and prevents unauthorized access by sensed spectrum data.…”

Section: ) Possible Blockchain-based Solutionsmentioning

confidence: 99%

A Survey on Blockchain for Dynamic Spectrum Sharing

Perera,

Ranaweera,

Kusaladharma

et al. 2024

IEEE Open J. Commun. Soc.

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The rapid increase in mobile users, the IoT, and data-hungry applications have brought forth unprecedented demand on the spectrum, which is scarce; on top of that, the existing static spectrum allocation schemes have resulted in a heavily underutilized spectrum which can be mitigated with a Dynamic Spectrum Access (DSA) scheme with unlicensed users gaining access to the idle spectrum bands of licensed spectrum users opportunistically. Such a DSA and Dynamic Spectrum Management (DSM) scheme would significantly increase spectral efficiency while facilitating new services and applications beyond 5G (B5G) networks. Even with access to new spectrum bands like terahertz (THz) and Visible light communication and enabling technologies such as Software Defined Networks (SDN) and Cognitive Radio (CR), implementing a fully realized DSM requires rapid sensing, coordination, and management, and sharing of idle spectrum bands in a fair manner while preserving the security and privacy aspects, limiting interferences. With their decentralized, immutable nature, blockchains promise the execution of spectrum access and sharing in a fully transparent, fair manner while preserving privacy and security. Furthermore, blockchain-based Smart Contracts (SCs) allow automation of DSM, cryptocurrencies, and tokens to facilitate the trading of spectrum and related resources. In addition to that, blockchains act as an interface for integrating AI and Machine Learning (ML) techniques into DSM, which provides a certain level of intelligence to the underlying architecture. Although several attempts have been carried out to analyze the research gaps in DSM, a comprehensive analysis addressing the blockchains as the primary solution to address DSM has not been carried out. In this survey, we address the potential of a blockchainbased approach toward realizing a decentralized DSM while presenting future directives to improve the use of blockchains for DSM.

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“…Blockchain offers a promising solution for overcoming spectrum monopoly and low spectrum utilization to increase communication system throughput and reduce latency. Various researches have recently focused on secure and dynamic spectrum trading [49]- [51], spectrum sensing [52]- [54], and spectrum management [55]- [58] using blockchain.…”

Section: Blockchain Applicationsmentioning

confidence: 99%

Blockchain Systems, Technologies, and Applications: A Methodology Perspective

Cao

Wang²,

Zhang

et al. 2023

IEEE Commun. Surv. Tutorials

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In the past decade, blockchain has shown a promising vision to build trust without any powerful third party in a secure, decentralized and scalable manner. However, due to the wide application and future development from cryptocurrency to the Internet of things, blockchain is an extremely complex system enabling integration with mathematics, computer science, communication and network engineering, etc. By revealing the intrinsic relationship between blockchain and communication, networking and computing from a methodological perspective, it provided a view to the challenge that engineers, experts and researchers hardly fully understand the blockchain process in a systematic view from top to bottom. In this article we first introduce how blockchain works, the research activities and challenges, and illustrate the roadmap involving the classic methodologies with typical blockchain use cases and topics. Second, in blockchain systems, how to adopt stochastic process, game theory, optimization theory, and machine learning to study the blockchain running processes and design the blockchain protocols/algorithms are discussed in details. Moreover, the advantages and limitations using these methods are also summarized as the guide of future work to be further considered. Finally, some remaining problems from technical, commercial and political views are discussed as the open issues. The main findings of this article will provide a survey from a methodological perspective to study theoretical model for blockchain fundamentals understanding, design network service for blockchain-based mechanisms and algorithms, as well as apply blockchain for the Internet of things, etc.

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Proof of Sense: A Novel Consensus Mechanism for Spectrum Misuse Detection

Cited by 13 publications

References 20 publications

Machine Learning Models for Fraud Detection: A Comprehensive Review and Empirical Analysis

Machine Learning Models for Fraud Detection: A Comprehensive Review and Empirical Analysis

A Survey on Blockchain for Dynamic Spectrum Sharing

Blockchain Systems, Technologies, and Applications: A Methodology Perspective

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