Cluster-Based Classification of Blockchain Consensus Algorithms

2023

Sensors

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.

Section: Smart Contracts Securitymentioning

confidence: 99%

Continuous and Secure Integration Framework for Smart Contracts

Reyes

Jimeno

2023

Sensors

“…Examples of these protocols are proof of burn [24], proof of space [25], proof-of-QoS (PoQ) [26], and a fair selection protocol [27]. These and other consensus protocols are presented in [16].…”

Section: Other Kinds Of Proof-based Consensus Algorithmsmentioning

confidence: 99%

“…Following a similar approach as in [16], we carried out a qualitative comparison between our proposed protocol and the following proof-based consensus protocols: Pure PoW [38], Cuckoo hash function-based PoW [39], Prime number finding-based PoW [23], Double puzzles-based PoW [40], Non-outsourceable puzzles [41], Bitcoin-NG [42], GHOST strategy [43], Generalized PoW [44], Pure PoS (Nextcoin) [45], State of the block-based PoS [19], PoS by coin flipping from many nodes [46], Delegated PoS [47], Coin age-based PoW difficulty re-designation (Ppcoin) [20], Stake-based PoW difficulty re-designation (Blackcoin) [21], Coin age with an exponential decay function [48], Combining PoW and PoS to append blocks sequentially [49], with difficulty adjustment [50], Proof of activity [51], Puzzles designed for human PoW [22], Proof of burn [24], Proof of space [25], Proof of elapsed time [52], Proof of luck [53], Multichain [54]; and the following Vote-based consensus protocols: Hyperledger with practical Byzantine fault tolerance [55], Symbiont, R3 Corda with BFT-SMaRt [56,57], Iroha with Sumeragi [58,59], Ripple [60], Stellar [61], Quorum with Raft [62], Chain [63].…”

Section: Comparison With Other Approachesmentioning

confidence: 99%

“…Proofof-work (PoW) and proof-of-stake (PoS) protocols are the most popular; however, they still have drawbacks. In particular, the consensus mechanism PoW is inefficient regarding energy consumed by its participants [16]. As a result of the efforts to improve the PoW and PoS protocols, the so-called alternative consensus protocols were born [17].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

On Proof-of-Accuracy Consensus Protocols

Aponte-Novoa

2022

Mathematics

Self Cite

Consensus protocols are a fundamental part of any blockchain; although several protocols have been in operation for several years, they still have drawbacks. For instance, some may be susceptible to a 51% attack, also known as a majority attack, which may suppose a high risk to the trustworthiness of the blockchains. Although this attack is theoretically possible, executing it in practice is often regarded as arduous because of the premise that, with sufficiently active members, it is not ’straightforward’ to have much computing power. Since it represents a possible vulnerability, the community has made efforts to solve this and other blockchain problems, which has resulted in the birth of alternative consensus protocols, e.g., the proof of accuracy protocol. This paper presents a detailed proposal of a proof-of-accuracy protocol. It aims to democratize the miners’ participation within a blockchain, control the miners’ computing power, and mitigate the majority attacks.

“…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

et al. 2022

Sensors

Self Cite

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.