On Proof-of-Accuracy Consensus Protocols

Aponte-Novoa, Fredy Andrés; Villanueva-Polanco, Ricardo

doi:10.3390/math10142504

Cited by 6 publications

(3 citation statements)

References 38 publications

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“…In addition to the security risks posed against distributed ledgers, such as 51% attack [ 54 ] against blockchain based on mining [ 55 , 56 ], as well as emerging threats as cryptojacking [ 57 ], there are other risks directly against smart contracts. The distributed and immutable characteristics of a smart contract in a blockchain had consequences when faults in them caused economic impacts in multiple cases [ 45 , 58 ].…”

Section: Smart Contracts Securitymentioning

confidence: 99%

Continuous and Secure Integration Framework for Smart Contracts

Reyes

Jimeno

Villanueva-Polanco

2023

Sensors

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As part of agile methodologies seen in the past few years, IT organizations have continuously adopted new practices in their software delivery life-cycle to improve both efficiency and effectiveness of development teams. Two of these practices are continuous integration and continuous deployment, which are part of the DevOps cycle which has helped organizations build software effectively and efficiently. These practices must be considered for new technologies such as smart contracts, where security concerns and bugs might cost more once deployed than traditional software. This paper states the importance of using a proper DevOps routine and how it is possible to apply this practice to a smart contract build. Specifically, this paper introduces a framework to implement DevOps for smart contracts development by describing multiple DevOps tools and their applicability to smart contract development.

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Section: Smart Contracts Securitymentioning

confidence: 99%

Continuous and Secure Integration Framework for Smart Contracts

Reyes

Jimeno

Villanueva-Polanco

2023

Sensors

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“…When all trustworthy nodes have an equal chance of making the final board, there is a fair consensus [29]. A proof-of-accuracy procedure was introduced, and a protocol was suggested by the author in [30]. The suggested protocol contains several aspects, such as coordinator selection, secret generation, part generation and distribution in the network, and participant competition to discover the shares to reconstruct the secret.…”

Section: Legacy Evolutions: Existing View and Emerging Technical Chal...mentioning

confidence: 99%

Blockchain-Enabled Decentralized Secure Big Data of Remote Sensing

et al. 2022

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Blockchain technology has emerged as a promising candidate for space exploration and sustainable energy systems. This transformative technology offers secure and decentralized strategies to process and manipulate space resources. Remote sensing provides viable potential with the coexistence of open data from various sources, such as short-range sensors on unmanned aerial vehicles (UAVs) or Internet-of-Things (IoT) tags and far-range sensors incorporated on satellites. Open data resources have most recently emerged as attractive connecting parties where owners have shown consent to share data. However, most data owners are anonymous and untrustworthy, which makes shared data likely insecure and unreliable. At present, there are several tools that distribute open data, serving as an intermediate party to link users with data owners. However, these platforms are operated by central authorities who develop guidelines for data ownership, integrity, and access, consequently restricting both users and data owners. Therefore, the need and feasibility of a decentralized system arise for data sharing and retrieving without involving these intermediate limiting parties. This study proposes a blockchain-based system without any central authority to share and retrieve data. Our proposed system features (i) data sharing, (ii) maintaining the historical data, and (iii) retrieving and evaluation of data along with enhanced security. We have also discussed the use of blockchain algorithms based on smart contracts to track space transactions and communications in a secure, verifiable, and transparent manner. We tested the suggested framework in the Windows environment by writing smart contracts prototype on an Ethereum TESTNET blockchain. The results of the study showed that the suggested strategy is efficient, practicable, and free of common security attacks and vulnerabilities.

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“…Cryptojacking is an illegal and unauthorized mining activity on the victim’s computer, using the computational power of the victim’s computer to extract cryptocurrencies, which generates large computational consumption, reducing the computational efficiency of the victim’s computer. Moreover, this attack may be used by a powerful attacker to increment their computationally power, posing a risk to any blockchain based on mining [ 1 , 2 , 3 , 4 , 5 , 6 , 7 ].…”

Section: Introductionmentioning

confidence: 99%

On Detecting Cryptojacking on Websites: Revisiting the Use of Classifiers

Aponte-Novoa

Álvarez

Villanueva-Polanco

et al. 2022

Sensors

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Cryptojacking or illegal mining is a form of malware that hides in the victim’s computer and takes the computational resources to extract cryptocurrencies in favor of the attacker. It generates significant computational consumption, reducing the computational efficiency of the victim’s computer. This attack has increased due to the rise of cryptocurrencies and their profitability and its difficult detection by the user. The identification and blocking of this type of malware have become an aspect of research related to cryptocurrencies and blockchain technology; in the literature, some machine learning and deep learning techniques are presented, but they are still susceptible to improvement. In this work, we explore multiple Machine Learning classification models for detecting cryptojacking on websites, such as Logistic Regression, Decision Tree, Random Forest, Gradient Boosting Classifier, k-Nearest Neighbor, and XGBoost. To this end, we make use of a dataset, composed of network and host features’ samples, to which we apply various feature selection methods such as those based on statistical methods, e.g., Test Anova, and other methods as Wrappers, not only to reduce the complexity of the built models but also to discover the features with the greatest predictive power. Our results suggest that simple models such as Logistic Regression, Decision Tree, Random Forest, Gradient Boosting, and k-Nearest Neighbor models, can achieve success rate similar to or greater than that of advanced algorithms such as XGBoost and even those of other works based on Deep Learning.

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On Proof-of-Accuracy Consensus Protocols

Cited by 6 publications

References 38 publications

Continuous and Secure Integration Framework for Smart Contracts

Continuous and Secure Integration Framework for Smart Contracts

Blockchain-Enabled Decentralized Secure Big Data of Remote Sensing

On Detecting Cryptojacking on Websites: Revisiting the Use of Classifiers

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