Detecting Covert Cryptomining using HPC
Ankit Gangwal, Samuele Giuliano Piazzetta, Gianluca Lain, Mauro Conti

TL;DR
This paper introduces a novel, hardware-based machine learning approach to detect covert cryptomining activities across various cryptocurrencies, achieving high accuracy and adaptability to new threats.
Contribution
The paper presents a generic detection method using Hardware Performance Counters and machine learning, effective across multiple cryptocurrencies and adaptable to zero-day threats.
Findings
Achieves near-perfect classification accuracy.
Detects cryptomining with as little as five seconds of data.
Effective across different processors and cryptocurrencies.
Abstract
Cybercriminals have been exploiting cryptocurrencies to commit various unique financial frauds. Covert cryptomining - which is defined as an unauthorized harnessing of victims' computational resources to mine cryptocurrencies - is one of the prevalent ways nowadays used by cybercriminals to earn financial benefits. Such exploitation of resources causes financial losses to the victims. In this paper, we present our novel and efficient approach to detect covert cryptomining. Our solution is a generic solution that, unlike currently available solutions to detect covert cryptomining, is not tailored to a specific cryptocurrency or a particular form of cryptomining. In particular, we focus on the core mining algorithms and utilize Hardware Performance Counters (HPC) to create clean signatures that grasp the execution pattern of these algorithms on a processor. We built a complete…
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