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- W3120557083 abstract "Cryptojacking, the appropriation of users' computational resources without their knowledge or consent to obtain cryp-tocurrencies, is a widespread attack, relatively easy to implement and hard to detect. Either browser-based or binary, cryptojacking lacks robust and reliable detection solutions. This paper presents a hybrid approach to detect cryptojacking where no previous knowledge about the attacks or training data is needed. Our Cryp-tojacking Intrusion Detection Approach, Cryingjackpot, extracts and combines flow and performance counter-based features, aggregating hosts with similar behavior by using unsupervised machine learning algorithms. We evaluate Cryingjackpot experimentally with both an artificial and a hybrid dataset, achieving F1-scores up to 97%." @default.
- W3120557083 created "2021-01-18" @default.
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- W3120557083 date "2020-11-24" @default.
- W3120557083 modified "2023-10-13" @default.
- W3120557083 title "CryingJackpot: Network Flows and Performance Counters against Cryptojacking" @default.
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- W3120557083 doi "https://doi.org/10.1109/nca51143.2020.9306698" @default.
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