Detecting A Crypto-mining Malware By Deep Learning Analysis

【Author】 Aljehani, Shahad; Alsuwat, Hatim

【Source】INTERNATIONAL JOURNAL OF COMPUTER SCIENCE AND NETWORK SECURITY

【影响因子】0.000

【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.

【Keywords】Ctypto-mining; Ctypto-jacking; Cryptography; Deep Learning; Detection

【发表时间】2022 JUN 30

【收录时间】2022-07-17

【文献类型】理论性文章

【主题类别】

区块链治理-技术治理-挖矿检测