A graph neural network with handcrafted temporal features identifies more Ethereum phishing addresses than one using raw transaction fields, though the phishing-class F1 is 0.28, not the reported 0.95.
Dissecting the Infrastructure Used in Web-based Cryptojacking: A Measurement Perspective
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abstract
This paper conducts a comprehensive examination of the infrastructure supporting cryptojacking operations. The analysis elucidates the methodologies, frameworks, and technologies malicious entities employ to misuse computational resources for unauthorized cryptocurrency mining. The investigation focuses on identifying websites serving as platforms for cryptojacking activities. A dataset of 887 websites, previously identified as cryptojacking sites, was compiled and analyzed to categorize the attacks and malicious activities observed. The study further delves into the DNS IP addresses, registrars, and name servers associated with hosting these websites to understand their structure and components. Various malware and illicit activities linked to these sites were identified, indicating the presence of unauthorized cryptocurrency mining via compromised sites. The findings highlight the vulnerability of website infrastructures to cryptojacking.
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cs.CR 1years
2025 1verdicts
REJECT 1representative citing papers
citing papers explorer
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Fishing for Phishers: Learning-Based Phishing Detection in Ethereum Transactions
A graph neural network with handcrafted temporal features identifies more Ethereum phishing addresses than one using raw transaction fields, though the phishing-class F1 is 0.28, not the reported 0.95.