摘要:针对当前恶意代码静态分析方法精度不足的问题，将恶意代码映射为无压缩的灰度图像，然后根据图像变换方法将图像变换为恒定大小的图像，使用方向梯度直方图提取图像的特征，最后提出一种基于深度森林的恶意代码分类方法.实验中选择不同家族的多个恶意代码样本进行分类，验证了该方法的有效性，并且实验结果优于近期提出的SPAM-GIST方法.



Abstract:Aiming at the problem of insufficient accuracy of current static classification method of malicious code, this study maps the malicious code into uncompressed gray-scale image. Then the image is transformed into a constant-size image according to the image transformation method, and the direction gradient histogram is used to extract the features of the image. Finally, a kind of malicious code classification method based on deep forest is proposed. Experiments on malicious code samples from different families verify the effectiveness of the proposed method and the results are superior to the recently proposed SPAM-GIST method.



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