Securing Majority-Attack in Blockchain Using Machine Learning and Algorithmic Game Theory: A Proof of Work

Dey, Somdip

doi:10.1109/ceec.2018.8674185

Cited by 73 publications

(34 citation statements)

References 19 publications

(1 reference statement)

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“…On the one hand, a significant amount of research is devoted to the security of blockchain-based systems. This is mainly due to the fact that in many cases, the well-known 51% majority attack still poses a risk to the integrity of the whole ecosystem [10], [11], [23]. Another important research direction in terms of security is the fork problem, which is leading to the emergence of a rivalling sibling of a given blockchain [25].…”

Section: Related Researchmentioning

confidence: 99%

Revealing the Character of Nodes in a Blockchain With Supervised Learning

2020

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The term blockchain has its roots in cryptocurrencies. However, its applications are now more widespread, and in many areas, this technology has become the foundation of the distributed ledger. The blockchain protocol assumes that all the participants of the system are both contributors and safeguards of this ledger, since the lack of a trusted third party requires other security precautions in order to maintain the consistency of transactions. In this work, we investigate whether for the participants of a blockchain-based system that does not require revealing the character explicitly, it can be discovered by other means. In order to verify this, we built and publicly released a dataset of nearly 9,000 addresses of nodes in the most popular cryptocurrency-Bitcoin, and then labelled them. These labels represent the character the nodes have in the network, e.g. miners or exchanges. We then developed a set of features that quantify the behaviour of nodes in the network and used supervised machine learning algorithms to find out whether the character of nodes can be revealed based on these features. Our results demonstrate, due to the F-score reaching over 95% in the best-performing algorithms, that it is hard to hide the role the node has in a blockchain-based network. These results indicate that to build trustworthy blockchain-based systems that fully comply with original blockchain assumptions, specific countermeasures are needed in order to preserve the desired level of anonymity.

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Section: Related Researchmentioning

confidence: 99%

Revealing the Character of Nodes in a Blockchain With Supervised Learning

2020

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“…A common case consists in trying to apply machine learning for crypto-trading, or extensively trying to predict the price of a cryptocurrency using machine learning [ 22 , 23 ]. Other works have focused on the application of artificial intelligence to blockchain security [ 24 ]. From a more philosophical perspective, Swan commented about “blockchain thinking”, the possibility of formulating thinking as a blockchain process [ 25 ].…”

Section: Related Workmentioning

confidence: 99%

Coin.AI: A Proof-of-Useful-Work Scheme for Blockchain-Based Distributed Deep Learning

Baldominos

Sáez

2019

Entropy

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One decade ago, Bitcoin was introduced, becoming the first cryptocurrency and establishing the concept of "blockchain" as a distributed ledger. As of today, there are many different implementations of cryptocurrencies working over a blockchain, with different approaches and philosophies. However, many of them share one common feature: they require proof-of-work to support the generation of blocks (mining) and, eventually, the generation of money. This proof-of-work scheme often consists in the resolution of a cryptography problem, most commonly breaking a hash value, which can only be achieved through brute-force. The main drawback of proof-of-work is that it requires ridiculously large amounts of energy which do not have any useful outcome beyond supporting the currency. In this paper, we present a theoretical proposal that introduces a proof-of-useful-work scheme to support a cryptocurrency running over a blockchain, which we named Coin.AI. In this system, the mining scheme requires training deep learning models, and a block is only mined when the performance of such model exceeds a threshold. The distributed system allows for nodes to verify the models delivered by miners in an easy way (certainly much more efficiently than the mining process itself), determining when a block is to be generated. Additionally, this paper presents a proof-of-storage scheme for rewarding users that provide storage for the deep learning models, as well as a theoretical dissertation on how the mechanics of the system could be articulated with the ultimate goal of democratizing access to artificial intelligence. Entropy 2019, 21, 723 2 of 17Additionally, in recent years many other cryptocurrencies have appeared whose workings are very similar to Bitcoin, although with some particularities and different philosophies. Probably, the most significant example is the Ether, the currency provided by the Ethereum platform, which was presented in 2013 by Buterin [7]. Again, this currency has seen its value increased significantly since it was born in July 2015, traded at about US$0.70 by August 2015 and with a value of more than US$220 by July 2019.Bitcoin, Ethereum, and many other cryptocurrencies of this kind work atop a technology commonly known as blockchain [8]. Blockchain technology allows to store transactions in blocks, which are placed sequentially, thus forming a chain. A cryptographic mechanism prevents the chain from being tampered, and the only operation that can alter the blockchain is appending a new block at the end of it. In other words, this mechanism avoids malicious activities that can try to alter or remove transactions that took place at some time in the past. Blockchains have been widely used with academic purposes, such as developing consensus protocols [9] or developing data exchange schemas for the internet of things [10]. Noticeably, they have also been used for not so honest purposes, and particularly some financial frauds, such as Ponzi schemes, have been detected in public networks [11].Although a blockchain can...

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“…Although there are standards available such as General Food Law in EU [4], Food Safety Modernization Act (FSMA) in US [5], which try to standardize traceability of digital information of food production in some of the stages of food supply chain, these standards are regional and currently there is no holistic standardization of tracking and recording data for food traceability purposes in all stages of food supply chain across the globe. Henceforth, in order to deal with such a challenge related to food safety, Blockchain Technology (BT) [6,7] may play a vital role in the traceability of food ingredients in recent times such that the consumers can trace the source of the food ingredients that they are buying/consuming. Traditionally many producers still record data of their production on papers, whereas, some producers digitize the production data, which doesn't enable interaction with other parties in the food system.…”

Section: Introductionmentioning

confidence: 99%

FoodSQRBlock: Digitizing food production & supply chain with blockchain & QR code in the cloud

Dey¹,

Saha²,

Singh³

et al. 2021

Preprint

Self Cite

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<div><div><div><p>Food safety is an important issue in today’s world. Traditional agri-food production system doesn’t offer easy traceability of the produce at any point of the supply chain, and hence, during a food-borne outbreak, it is very difficult to sift through food production data to track produce and origin of the outbreak. In recent years, blockchain based food production system has resolved this challenge, however, none of the proposed methodologies makes the food production data easily accessible, traceable and verifiable by consumers or producers using mobile/edge devices. In this paper, we propose FoodSQRBlock (Food Safety Quick Response Block), a blockchain technology based framework, which digitizes the food production information, and makes it easily accessible, traceable and verifiable by the consumers and producers by using QR codes. We also propose a large scale integration of FoodSQRBlock in the cloud to show the feasibility and scalability of the framework, and experimental evaluation to prove that.</p></div></div></div>

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Securing Majority-Attack in Blockchain Using Machine Learning and Algorithmic Game Theory: A Proof of Work

Cited by 73 publications

References 19 publications

Revealing the Character of Nodes in a Blockchain With Supervised Learning

Revealing the Character of Nodes in a Blockchain With Supervised Learning

Coin.AI: A Proof-of-Useful-Work Scheme for Blockchain-Based Distributed Deep Learning

FoodSQRBlock: Digitizing food production & supply chain with blockchain & QR code in the cloud

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