• International Journal of Computer Science & Network Security
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• v.22 no.6
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• pp.172-180
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• 2022

Crypto-mining malware (known as crypto-jacking) is a novel cyber-attack that exploits the victim's computing resources such as CPU and GPU to generate illegal cryptocurrency. The attacker get benefit from crypto-jacking by using someone else's mining hardware and their electricity power. This research focused on the possibility of detecting the potential crypto-mining malware in an environment by analyzing both static and dynamic approaches of deep learning. The Program Executable (PE) files were utilized with deep learning methods which are Long Short-Term Memory (LSTM). The finding revealed that LTSM outperformed both SVM and RF in static and dynamic approaches with percentage of 98% and 96%, respectively. Future studies will focus on detecting the malware using larger dataset to have more accurate and realistic results.

• Convergence Security Journal
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• v.24 no.2
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• pp.19-29
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• 2024

A crypto-jacking attack is an attack that infringes on the availability of users by stealing computing resources required for cryptocurrency mining. The target of the attack is gradually diversifying from general desktop or server environments to cloud environments. Therefore, it is essential to apply a crypto-minor detection technique suitable for various computing environments. However, since the existing detection methodologies have only been detected in a specific environment, comparative analysis has not been properly performed on the methodologies that can be applied to each environment. Therefore, in this study, classification criteria for conventional crypto-minor detection techniques are established, and a complex and integrated detection framework applicable to the cloud environment is presented through in-depth comparative analysis of existing crypto-minor detection techniques based on different experimental environments and datasets.