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Dissecting the Infrastructure Used in Web-based Cryptojacking: A Measurement Perspective

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arxiv 2408.03426 v1 pith:DL37M5E5 submitted 2024-08-06 cs.CR

classification cs.CR
keywords cryptojackingactivitiessiteswebsitescryptocurrencyidentifiedinfrastructuremalicious
verification ladder T0 review T1 audit T2 compute T3 formal

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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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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Fishing for Phishers: Learning-Based Phishing Detection in Ethereum Transactions

    cs.CR 2025-04 reject novelty 4.0 of 10

    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.

  2. Evaluating the Vulnerability of ML-Based Ethereum Phishing Detectors to Single-Feature Adversarial Perturbations

    cs.CR 2025-04 reject novelty 3.0 of 10

    Simple single-feature perturbations, such as shifted timestamps and altered values, sharply reduce the accuracy of Random Forest, Decision Tree, and KNN Ethereum phishing detectors, with adversarial training reported ...

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