Towards Scalable Blockchain Analysis

International audienceAnalysing the blockchain is becoming more and more relevant for detecting attacks and frauds on cryptocurrency exchanges and smart contract activations. However, this is a challenging task due to the continuous growth of the blockchain. For example, in early 2017 Ethereum was estimated to contain approximately 300GB of data [4], a number that keeps growing day after day. In order to analyse such ever-growing amount of data, this paper argues that blockchain analysis should be treated as a novel type of application for Big Data platforms. We also explore the application of parallelization techniques from the Big Data domain, in particular Map/Reduce, to extract and analyse information from the blockchain. We show that our approach significantly improves the index generation by 7.77 times, with a setup of 20 worker nodes, 1 Ethereum node and 1 Database node. We also share our findings of our massively parallel setup for querying Ethereum in terms of architecture and the bottlenecks. This should help researchers setup similar infrastructures for analysing the blockchain in the future

Analysing Microsoft Access Projects: Building a model in a Partially Observable Domain

International audienceDue to the technology evolution, every IT Company migrates their software systems at least once. Reengineering tools build system models which are used for running software analysis. These models are traditionally built from source code analysis and information accessible by data extractors (that we call such information observable). In this article we present the case of Microsoft Access projects and how this kind of project is partially observable due to proprietary storing formats. We propose a novel approach for building models that allows us to overcome this problem by reverse engineering the development environment runtime through the usage of Microsoft COM interface. We validate our approach and implementation by fully replicating 10 projects, 8 of them industrial, based only on our model information. We measure the repli-cation performance by measuring the errors during the process and completeness of the product. We measure the replication error, by tracking replication operations. We used the scope and completeness measure to enact this error. Completeness is measured by the instrumentation of a simple and scoped diff based on a third source of information. We present extensive results and interpretations. We discuss the threats to validity, the possibility of other approaches and the technological restrictions of our solution

Detecting Selfish Mining Attacks Against a Blockchain Using Machine Learing

Selfish mining is an attack against a blockchain where miners hide newly discovered blocks instead of publishing them to the rest of the network. Selfish mining has been a potential issue for blockchains since it was first discovered by Eyal and Sirer. It can be used by malicious miners to earn a disproportionate share of the mining rewards or in conjunction with other attacks to steal money from network users. Several of these attacks were launched in 2018, 2019, and 2020 with the attackers stealing as much as $18 Million. Developers made several different attempts to fix this issue, but the effectiveness of the fixes is currently unknown. Despite the known vulnerability, there is little researching into detecting these attacks either historically or in real-time. In this research, we build a program to gather data from known selfish mining attacks against the Ethereum Classic blockchain. We then use this data to train a machine-learning algorithm to discover the important features for detecting selfish mining

BLOCKCHAIN-BASED ACCESS AND USAGE CONTROL OF CLOUD-BASED DIGITAL TWINS

This article introduces a novel data-feeding system that integrates Ethereum blockchain, smart contracts, and Chainlink, aiming to efficiently acquire, back up, and utilize digital twin resources. In the Internet of Things (IoT) realm, digital twin resources have been extensively adopted. However, precise data sharing and data privacy management remain pivotal challenges. Through the techniques presented in this paper, we offer a decentralized and secure method for data acquisition, accompanied by a comprehensive access control system to ensure data security. Our system employs Ethereum smart contracts to clearly stipulate the rules and conditions for accessing digital twin resources. Furthermore, by incorporating Chainlink technology, we have developed specialized external connectors, enabling smart contracts to pull Off-chain data from the digital twin cloud platform directly. All access controls and operations are logged on the Ethereum blockchain, ensuring historical security and traceability. Our experiments validate the efficacy of our approach, confirming that the system can stably acquire, back up, and utilize digital twin resource data. The findings indicate that this method provides a reliable and scalable data management and control solution in IoT contexts. In conclusion, by merging blockchain, smart contract, and Chainlink technologies, we present an innovative and promising strategy for acquiring, backing, utilizing, and securing IoT resources

Towards Scalable Blockchain Analysis

International audienceAnalysing the blockchain is becoming more and more relevant for detecting attacks and frauds on cryptocurrency exchanges and smart contract activations. However, this is a challenging task due to the continuous growth of the blockchain. For example, in early 2017 Ethereum was estimated to contain approximately 300GB of data [4], a number that keeps growing day after day. In order to analyse such ever-growing amount of data, this paper argues that blockchain analysis should be treated as a novel type of application for Big Data platforms. We also explore the application of parallelization techniques from the Big Data domain, in particular Map/Reduce, to extract and analyse information from the blockchain. We show that our approach significantly improves the index generation by 7.77 times, with a setup of 20 worker nodes, 1 Ethereum node and 1 Database node. We also share our findings of our massively parallel setup for querying Ethereum in terms of architecture and the bottlenecks. This should help researchers setup similar infrastructures for analysing the blockchain in the future

Towards Scalable Blockchain Analysis

International audienceAnalysing the blockchain is becoming more and more relevant for detecting attacks and frauds on cryptocurrency exchanges and smart contract activations. However, this is a challenging task due to the continuous growth of the blockchain. For example, in early 2017 Ethereum was estimated to contain approximately 300GB of data [4], a number that keeps growing day after day. In order to analyse such ever-growing amount of data, this paper argues that blockchain analysis should be treated as a novel type of application for Big Data platforms. We also explore the application of parallelization techniques from the Big Data domain, in particular Map/Reduce, to extract and analyse information from the blockchain. We show that our approach significantly improves the index generation by 7.77 times, with a setup of 20 worker nodes, 1 Ethereum node and 1 Database node. We also share our findings of our massively parallel setup for querying Ethereum in terms of architecture and the bottlenecks. This should help researchers setup similar infrastructures for analysing the blockchain in the future