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Nowadays, machine learning based methods are among the most popular ones for malware detection. However, most of the previous works use a single type of features, dynamic or static, and take them to build a binary classification model. These methods have limited ability to depict characteristic malware behaviors and suffer from insufficiently sampled benign samples and extremely imbalanced training...
Capturing behavior of malware is one of the essential prerequisites for dynamic malware analysis. In this paper, we study and design a system called Osiris, which makes use of virtual machine technique to capture malware behavior. In particularly, we monitor Windows API calls invoked by the process under analysis (or target program) to rebuild its behaviors. The monitor is implemented at the virtual...
Malware is one of the most serious threats to the security of computer systems. Many approaches have been provided and various systems have been designed to detect intrusion from anomalous behavior of system calls which provide the interface between a process and the operating system. Though these techniques look quite effective, a key element seems to be missing - the inclusion and utilization of...
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