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toymalwareclassification's Introduction

微软恶意代码分类

代码说明

  • randomsubset.py 抽取训练子集
  • asmimage.py ASM文件图像纹理特征
  • opcode_n-gram.py Opcode n-gram特征
  • firstrandomforest.py 基于ASM文件图像纹理特征的随机森林
  • secondrandomforest.py 基于Opcode n-gram特征特征的随机森林
  • combine.py 将两种类型的特征结合

运行说明

  1. 将完整的训练数据集解压,修改randomsubset.py中的路径并运行
  2. 修改asmimage.pyopcode_n-gram.py中的路径,并运行run.sh,耐心等待即可看到结果

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toymalwareclassification's Issues

通过该方案实现bug识别

hi,博主:
有幸读过乌云上的《利用机器学习进行恶意代码分类》的文章,从中得到了一些启发。有些疑问想请教一下~

  1. 能否通过恶意代码图像的方式获取常规出现bug的代码图像呢?
  2. OpCode n-gram能否获取当前区域代码的上下文?
  3. 能否推荐决策树和随机森林相关比较好的模型?

期待回复~

微软提供的数据问题

您好~
看了您的关于恶意代码检测方面的分享,本人做实验验证的时候发现,微软提供的数据压缩文件不能用了(下载下来后提示数据格式出错),请问您有没有遇到过这种情况?

dataset

Hi,
I am new to malware detection. In the process of experiments according to your competition published in Kaggle. I encountered several problems:

The first,  Is the tag of test data not showing? The train and test data cannot uncompress. Can you share the normal dataset with me? Thank you very much!

 I sincerely hope you will soon reply me. Thank you very much!

Vinu

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