What is it about?
This study presents CryptojackingTrap, an innovative algorithm designed to detect cryptojacking, where cybercriminals exploit victims’ computing resources to mine cryptocurrencies without their consent. Building on the previous BotcoinTrap work, this solution is not limited to Bitcoin but extends to all cryptocurrencies. With advanced memory trace analysis and a nature-inspired algorithm, CryptojackingTrap accurately detects cryptojacking, even against evasive malware that lowers its hash rate to avoid detection. The architecture is open-source and highly extensible, making it adaptable to future developments in cryptojacking techniques.
Featured Image
Why is it important?
CryptojackingTrap addresses a critical challenge in cybersecurity: detecting cryptojacking with high accuracy and resistance to evasion. While many detection techniques exist, most are circumvented by attackers. This paper, published in the top-ranked journal TIFS (Transactions on Information Forensics and Security) in 2024, introduces a novel solution that stands out for its robustness and zero false-positive and false-negative rates. It sets a new benchmark for cryptojacking detection.
Perspectives
CryptojackingTrap pushes the boundaries of cryptojacking detection by correlating low-level memory traces with cryptocurrency network behavior, providing an exceptionally accurate and evasion-resistant solution. Even when attackers reduce their mining hash rates by ten times, CryptojackingTrap detects them effectively. This approach, rooted in game theory, raises the cost of attacks, making them economically unviable for cybercriminals. As the first generalized detection method applicable to all cryptocurrencies, this research opens up vast opportunities for future security enhancements, offering a new foundation for protecting against emerging cryptojacking threats. With its open-source architecture, this work invites further collaboration and development.
Ms. Atefeh Zareh Chahoki
Read the Original
This page is a summary of: CryptojackingTrap: An Evasion Resilient Nature-Inspired Algorithm to Detect Cryptojacking Malware, IEEE Transactions on Information Forensics and Security, January 2024, Institute of Electrical & Electronics Engineers (IEEE),
DOI: 10.1109/tifs.2024.3353072.
You can read the full text:
Contributors
The following have contributed to this page
