Dissecting Ponzi schemes on Ethereum: identification, analysis, and impact
This paper made a deep survey about Ponzi scheme by analyzing contracts' vulnerabilities,Comparing the proportion of in and out transactions of ponzi schme smart contracts, providing early warning for vulnerable users and presenting some common methods to collecting Ponzi schemes.The contributions of this paper give great help for people to master the features of the Ethereum Ponzi schemes.
Selfish Mining in Ethereum
This paper analyzes the selfish mining in Etheruem, which is different from that in Bitcoin
Analyzing Ethereum’s Contract Topology
This article studied the features of Ethereum, a new cryptocurrency and decentralized application platform with the function of smart contracts. This article USES the geth client to document statistics on how contracts are created in Ethereum and how users and contracts interact. It does learning analysis at the bytecode level. and the analysis results include code reuse, clustering statistics and so on, which are of great significance in vulnerability analysis and contract design.
Understanding Ethereum via Graph Analysis
Towards Analyzing the Complexity Landscape ofSolidity Based Ethereum Smart Contracts
This paper analyzes over 40 thousand Solidity source files, using some well-known OO(object oriented) metrics. The results suggest that smart contract programs are short, neither overly complex nor coupled too much, do not rely heavily on inheritance, and either quite well-commented or not commented at all. Moreover, smart contracts could benefit from an external library and dependency management mechanism, as more than 85% of the defined libraries in Solidity files code the same functionalities.
S-gram: Towards Semantic-Aware Security Auditing for Ethereum Smart Contracts
In this paper, the authors present the S-gram semantic-aware security auditing technique for Ethereum smart contracts. The S-gram highlighted the insight that statistical abnormality is very much likely to indicate the existence of vulnerabilities. It's novel that the paper introduces the natural language processing technic to analysis the smart contract code. The n-gram language model achieved an over 90% accuracy in identifying different types of potential vulnerabilities.
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Smart Contract (10)
data mining (5)
Transaction Graph (4)
Ponzi scheme (3)
Bitcoin Clustering (2)
Peer to Peer (2)
Generals Byzantine Problem (2)