The microvelocity of money in Ethereum by Francesco Maria De Collibus, Carlo Campajola, Claudio J. Tessone

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Survey on Ethereum phishing detection technology by Zhao CAI, Tao JING, Shuang REN

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Conformance evaluation of the top‐100 Ethereum token smart contracts with Ethereum Request for Comment‐20 functional specifications by Hyeon‐Ah Moon, Sooyong Park

“…Abstract This study is an attempt to evaluate the compliance problem regarding the current status of the establishment of agreed‐upon interfaces for tokens, which are the most important type of applications of the Ethereum blockchain. The authors analysed the existing application programming interface standard interface called Ethereum Request for Comment 20 (ERC‐20), proposed functional ERC‐20 specifications, and evaluated the real‐world token smart contracts that have been deployed and used for transactions on a blockchain. …”
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Bytecode-based approach for Ethereum smart contract classification by Dan LIN, Kaixin LIN, Jiajing WU, Zibin ZHENG

“…In recent years, blockchain technology has been widely used and concerned in many fields, including finance, medical care and government affairs.However, due to the immutability of smart contracts and the particularity of the operating environment, various security issues occur frequently.On the one hand, the code security problems of contract developers when writing contracts, on the other hand, there are many high-risk smart contracts in Ethereum, and ordinary users are easily attracted by the high returns provided by high-risk contracts, but they have no way to know the risks of the contracts.However, the research on smart contract security mainly focuses on code security, and there is relatively little research on the identification of contract functions.If the smart contract function can be accurately classified, it will help people better understand the behavior of smart contracts, while ensuring the ecological security of smart contracts and reducing or recovering user losses.Existing smart contract classification methods often rely on the analysis of the source code of smart contracts, but contracts released on Ethereum only mandate the deployment of bytecode, and only a very small number of contracts publish their source code.Therefore, an Ethereum smart contract classification method based on bytecode was proposed.Collect the Ethereum smart contract bytecode and the corresponding category label, and then extract the opcode frequency characteristics and control flow graph characteristics.The characteristic importance is analyzed experimentally to obtain the appropriate graph vector dimension and optimal classification model, and finally the multi-classification task of smart contract in five categories of exchange, finance, gambling, game and high risk is experimentally verified, and the F1 score of the XGBoost classifier reaches 0.913 8.Experimental results show that the algorithm can better complete the classification task of Ethereum smart contracts, and can be applied to the prediction of smart contract categories in reality.…”
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Defense scheme for the world state based attack in Ethereum by Zhen GAO, Dongbin ZHANG, Xiao TIAN

Subjects: “…Ethereum…”
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Sentralisasi Pengawasan Informasi Jaringan Menggunakan Blockchain Ethereum by Muhammad Fajar Sidiq, Akbari Indra Basuki, Halim Firdaus, Muhammad Aldi Baihaqi

“…Nevertheless, the proposed method is applicable only for the Proof-of-Authority (PoA) Ethereum networks. It is not feasible for the Ethereum main network that uses Proof-of-Work (PoW) due to the high cost of Ether.   …”
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Using the Ethereum Blockchain in the Internet of Things Network for IT Diagnostics by U. A. Vishniakou, YiWei Xia, Chuyue Yu

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Bitcoin ve Ethereum Piyasasında Takvim Anomalilerinin İncelenmesi by Arzu Özmerdivanlı

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Analysis of Ethereum transactions during the prevention and investigation of criminal offenses by V. V. Nosov, O. V. Manzhai, Ye. V. Panchenko

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ContractGuard:defend Ethereum smart contract with embedded intrusion detection by Gansen ZHAO, Zhijian XIE, Xinming WANG, Jiahao HE, Chengzhi ZHANG, Chengchuang LIN, ZHOU Ziheng, Bingchuan CHEN, RONG Chunming

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Safe havens for Bitcoin and Ethereum: evidence from high-frequency data by Fahad Ali, Muhammad Usman Khurram, Ahmet Sensoy

“…We find that FAANG stocks can be considered (at least weak) safe havens for Bitcoin and Ethereum during the sample period. Our subperiod analyses reveal that the safe-haven role of FAANG stocks, specifically for Bitcoin, has noticeably increased. …”
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Detecting Rug-Pull: Analyzing Smart Contract Backdoor Codes in Ethereum by Kwan Woo Yu, Byung Mun Lee

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Utilizing the Ethereum blockchain for retrieving and archiving augmented reality surgical navigation data by Sai Batchu, Michael J. Diaz, Lauren Ladehoff, Kevin Root, Brandon Lucke-Wold

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Smart Contract Penyimpanan Data Genetika Manusia Berbiaya Murah pada Blockchain Ethereum by Tri Stiyo Famuji, Herman Herman, Sunardi Sunardi

“…Data genetik tersebut disimpan dalam Smart contract pada blockchain Ethereum yang ditulis menggunakan bahasa pemrograman Solidity. …”
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Honeypot contract detection method for Ethereum based on source code structure and graph attention network by Youwei WANG, Yudong HOU, Lizhou FENG

Subjects: “…Ethereum…”
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THE FUTURE OF THE CRYPTOCURRENCY MARKET by A. Berdyshev

Subjects: “…ethereum…”
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Formal Verification of ERC-Based Smart Contracts: A Systematic Literature Review by Rim Ben Fekih, Mariam Lahami, Salma Bradai, Mohamed Jmaiel

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Exploiting Blockchain Technology for Enhancing Digital Twins’ Security and Transparency by Alessio Ferone, Stefano Verrilli

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Doubt in the Legal Capacity of Smart Contract’s Parties and the Possibility of Applying the Principle of Correctness in the Assumption of Non-Qualification by Mohammad Hasan Taj Langerudi, Farzin Dehdar

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Honeypot contract detection of blockchain based on deep learning by Hongxia ZHANG, Qi WANG, Dengyue WANG, Ben WANG

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