会议论文详细信息
2017 3rd International Conference on Applied Materials and Manufacturing Technology | |
A Shellcode Detection Method Based on Full Native API Sequence and Support Vector Machine | |
Cheng, Yixuan^1 ; Fan, Wenqing^1 ; Huang, Wei^1 ; An, Jing^1 | |
A College of Computer, Communication University of China, Beijing | |
100024, China^1 | |
关键词: API calls; Detection rates; Dynamic characteristics; Dynamic monitoring; High-accuracy; Sequence features; Shellcode; Shellcode detections; | |
Others : https://iopscience.iop.org/article/10.1088/1757-899X/242/1/012124/pdf DOI : 10.1088/1757-899X/242/1/012124 |
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来源: IOP | |
【 摘 要 】
Dynamic monitoring the behavior of a program is widely used to discriminate between benign program and malware. It is usually based on the dynamic characteristics of a program, such as API call sequence or API call frequency to judge. The key innovation of this paper is to consider the full Native API sequence and use the support vector machine to detect the shellcode. We also use the Markov chain to extract and digitize Native API sequence features. Our experimental results show that the method proposed in this paper has high accuracy and low detection rate.
【 预 览 】
Files | Size | Format | View |
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A Shellcode Detection Method Based on Full Native API Sequence and Support Vector Machine | 193KB | download |