What is it about?
This study introduces an innovative solution to detect Bitcoin miners that covertly use users' CPU resources for illegal mining activities without their consent. While many techniques exist to detect such malware, attackers develop countermeasures to evade them. In this research, the authors explore multiple approaches and propose a novel, hard-to-evade solution, designed to prevent attackers from easily hiding their activity. This work is highly original, as no similar solution has been published in the literature before.
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Why is it important?
The importance of this research lies in its unique, evasion-resilient nature. The proposed solution addresses the weaknesses of previous approaches and introduces a new standard in detecting Bitcoin mining malware that attackers find difficult to bypass.
Perspectives
This research leverages low-level memory trace debugging, correlating it with Bitcoin network activity to extract critical features that are nearly impossible to evade without significant effort, making the attack unprofitable. This is the core of security: making attacks so challenging that they become uninteresting to rational attackers from a game-theory perspective. This approach marks the beginning of a new direction for dealing with cryptominers, opening a promising path for the security community to explore and build upon.
Ms. Atefeh Zareh Chahoki
Read the Original
This page is a summary of: BotcoinTrap: Detection of Bitcoin Miner Botnet Using Host Based Approach, August 2018, Institute of Electrical & Electronics Engineers (IEEE),
DOI: 10.1109/iscisc.2018.8546867.
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