Detecting Malicious Accounts on the Ethereum Blockchain with Supervised Learning

Kumar, Nitesh; Singh, Ajay Pratap; Handa, Anand; Shukla, Sandeep K.

doi:10.1007/978-3-030-49785-9_7

Cited by 37 publications

(14 citation statements)

References 3 publications

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“…Limitations. In this paper, we use the labels from Etherscan as our ground truth, similar to previous works [3], [48], [79]. Etherscan is trusted by the community that it has a clear and comprehensive view of the labels, since Etherscan has reliable data sources.…”

Section: Discussionmentioning

confidence: 99%

An Empirical Study on Ethereum Private Transactions and the Security Implications

Lyu¹,

Zhang²,

Zhang³

et al. 2022

Preprint

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Recently, Decentralized Finance (DeFi) platforms on Ethereum are booming, and numerous traders are trying to capitalize on the opportunity for maximizing their benefits by launching front-running attacks and extracting Miner Extractable Values (MEVs) based on information in the public mempool. To protect end users from being harmed and hide transactions from the mempool, private transactions, a special type of transactions that are sent directly to miners, were invented. Private transactions have a high probability of being packed to the front positions of a block and being added to the blockchain by the target miner, without going through the public mempool, thus reducing the risk of being attacked by malicious entities.Despite the good intention of inventing private transactions, due to their stealthy nature, private transactions have also been used by attackers to launch attacks, which has a negative impact on the Ethereum ecosystem. However, existing works only touch upon private transactions as by-products when studying MEV, while a systematic study on private transactions is still missing. To fill this gap and paint a complete picture of private transactions, we take the first step towards investigating the private transactions on Ethereum. In particular, we collect largescale private transaction datasets and perform analysis on their characteristics, transaction costs and miner profits, as well as security impacts. This work provides deep insights on different aspects of private transactions.

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Section: Discussionmentioning

confidence: 99%

An Empirical Study on Ethereum Private Transactions and the Security Implications

Lyu¹,

Zhang²,

Zhang³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…1) Security enhancement: Machine learning can provide blockchain with intelligent means of detecting anomalies. In particular, machine learning can be applied to identify attack types and malicious nodes by monitoring and classifying the motives of participants, thus effectively preventing attacks [183]- [186].…”

Section: A Classical Issuesmentioning

confidence: 99%

Blockchain Systems, Technologies, and Applications: A Methodology Perspective

Cao

Wang²,

Zhang

et al. 2023

IEEE Commun. Surv. Tutorials

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In the past decade, blockchain has shown a promising vision to build trust without any powerful third party in a secure, decentralized and scalable manner. However, due to the wide application and future development from cryptocurrency to the Internet of things, blockchain is an extremely complex system enabling integration with mathematics, computer science, communication and network engineering, etc. By revealing the intrinsic relationship between blockchain and communication, networking and computing from a methodological perspective, it provided a view to the challenge that engineers, experts and researchers hardly fully understand the blockchain process in a systematic view from top to bottom. In this article we first introduce how blockchain works, the research activities and challenges, and illustrate the roadmap involving the classic methodologies with typical blockchain use cases and topics. Second, in blockchain systems, how to adopt stochastic process, game theory, optimization theory, and machine learning to study the blockchain running processes and design the blockchain protocols/algorithms are discussed in details. Moreover, the advantages and limitations using these methods are also summarized as the guide of future work to be further considered. Finally, some remaining problems from technical, commercial and political views are discussed as the open issues. The main findings of this article will provide a survey from a methodological perspective to study theoretical model for blockchain fundamentals understanding, design network service for blockchain-based mechanisms and algorithms, as well as apply blockchain for the Internet of things, etc.

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“…Kumar et al [65] proposed a supervised machine-learningbased approach to detect malicious accounts in Ethereum. The proposed approach has achieved high scores of detection accuracy of 96.21% and a false positive rate of 3% by analyzing both EOA and contract accounts.…”

Section: F Malicious Account/entities Detectionmentioning

confidence: 99%

Applicability of Intrusion Detection System on Ethereum Attacks: A Comprehensive Review

et al. 2022

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Ethereum attracts more investors, researchers, and even scammers for many reasons; this is the first platform that enables the new Decentralized Applications (DApps) to run on top of the blockchain network. However, the rich semantics and applications of DApps inevitably introduce many security issues that have grabbed significant attention from industry and academics due to their destructive impact on DApps in recent years. Therefore, there is a vital need to study the applicability of Intrusion Detection System in detecting Ethereum-based attacks. Hence, this paper is among the first comprehensive review that studies the applicability of IDS in detecting Ethereum-based attacks. In addition, this paper lists all the potential attacks on Ethereum passing through the vulnerabilities that cause those attacks and ending with the consequences of each attack. Besides, this paper analyses all the IDS-based related works of Ethereum attacks detection since the Ethereum platform was launched in 2015. Finally, this paper discusses the open issues regarding vulnerabilities and attacks, challenges, and future directions.

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Detecting Malicious Accounts on the Ethereum Blockchain with Supervised Learning

Cited by 37 publications

References 3 publications

An Empirical Study on Ethereum Private Transactions and the Security Implications

An Empirical Study on Ethereum Private Transactions and the Security Implications

Blockchain Systems, Technologies, and Applications: A Methodology Perspective

Applicability of Intrusion Detection System on Ethereum Attacks: A Comprehensive Review

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