Cryptocurrency malware detection in real
Since cryptocurrency malware threats perform a legal process, it is a challenging task to detect this type of threat by a manual or heuristic method. In this paper, we propose a novel deep Recurrent Neural Network (RNN) learning model for hunting cryptocurrency malware threats.
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A Holistic Intelligent Cryptojacking Malware Detection System
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Performance of ML-based behavioral malware detection under different settings. Malware detection is often performed at endpoints where an endpoint security solution (quarantine, ) monitors host devices to detect threats in real time. In practice, these solutions rely on a chain of techniques (vmray, ), including static analysis and dynamic ...
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Cryptocurrency Mining Malware Detection Based on Behavior Pattern and ...
Miner malware has been steadily increasing in recent years as the value of cryptocurrency rises, which poses a considerable threat to users' device security. Miner malware has obvious behavior patterns in order to participate in blockchain computing. However, most miner malware detection methods use raw bytes feature and sequential opcode as detection features. It is difficult for these ...
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