Is Emulating "Binary Grep in Eyes" Possible with Machine Learning?
Is Emulating "Binary Grep in Eyes" Possible with Machine Learning?
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DOI：
10.1109/candar.2017.19
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发表时间：
2017-11
期刊：
影响因子：
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通讯作者：
M. Mimura;Yuhei Otsubo;Hidema Tanaka;A. Goto
中科院分区：
文献类型：
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作者：
M. Mimura;Yuhei Otsubo;Hidema Tanaka;A. Goto
网络安全领域的人才非常缺乏。特别是专家或白黑客的数量不足。他们拥有令人难以置信的技能，例如“眼睛中的二进制 grep”，没有人无法逻辑地解释它为什么或如何工作。 “Binary grep in eyes”是一种用人眼模拟在二进制文件中执行GREP命令的技能。一般来说，他们的技能很难自动化。 \par 本文提出了一些模拟“眼睛中的二进制 grep”的方法，以使用卷积神经网络（CNN）检测看不见的恶意文档文件。 CNN 通常与图像识别领域的创新联系在一起，并且比之前的几种现有模型取得了更好的结果。然后，本文根据实际的恶意文档文件创建了数据集，并计算了精度、召回率和 F 度量来评估我们的方法。因此，我们的方法有可能模拟“眼睛中的二进制 grep”并检测看不见的恶意文档文件中的 shellcode。
Talented people who work in the fields of cybersecurity are greatly lacking. In particular, there are insufficient experts or white hackers. They have incredible skills such as "binary grep in eyes", which nobody cannot explain logically why or how does it work. "Binary grep in eyes" is a skill to emulate executing GREP command in binary files with human eyes. In general, it is difficult to automate their skills. \par This paper proposes some methods to emulate "binary grep in eyes" to detect unseen malicious document files with Convolutional Neural Network (CNN). CNN is commonly linked with innovations in the fields of image recognition and achieves superior results over several prior existing models. Then this paper created the dataset from actual malicious document files in the wild, and calculated the Precision, the Recall and the F-measure to evaluate our method. As the result, there is a possibility that our method can emulate "binary grep in eyes" and detect the shellcode in unseen malicious document files.