Eugenio Emmolo
“Design and Implementation of Machine Learning Algorithms for Web Cryptomining Detection”.
Rel. Marco Mellia, Stefano Traverso. Politecnico di Torino, Master of science program in Computer Engineering, 2019
Abstract
“Design and Implementation of Machine Learning Algorithms for Web Cryptomining Detection” In the last years, cryptocurrencies like Bitcoin, Monero and Ethereum have gained popularity since they provide a valid alternative to the centralized banking system and an advantageous context for financial speculation. A core part in the cryptocurrency structure is the mining process, in which a computationally heavy cryptographic problem has to be solved in order to validate a group of online transactions and generate new currency. As this mechanism establishes a reward for each problem correctly solved, some ill-intentioned users, in place of using their own machines, started to make website visitors silently running some cryptomining code on their devices, creating as a matter of fact a new source of profit.
This process, meant to exploit third-party device resources, has been called 'cryptojacking' or 'drive-by mining': it consists in a new web threat that aims at covertly highjacking users computational power to mine cryptocurrency while they are browsing an infected website; as reported by the majority security providers in the time period (2017-2018), crypto-highjacking attacks became highly widespread and frequent, striking vulnerable websites and causing annoying problems to users surfing the Internet
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