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Title |
Detecting A Crypto-mining Malware By Deep Learning Analysis |
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Author |
Shahad Aljehani and Hatim Alsuwat |
| Citation |
Vol. 22 No. 6 pp. 172-180 |
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Abstract |
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. |
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Keywords |
Crypto-mining, Crypto-jacking, Cryptography, Deep Learning, Detection |
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