论文标题
关于欺骗恶意软件分类和部分注射
On deceiving malware classification with section injection
论文作者
论文摘要
我们研究了如何修改可执行文件以欺骗恶意软件分类系统。这项工作的主要贡献是一种方法，可以随机注入恶意软件文件，并将其用作攻击以降低分类准确性，也可以用作防御方法，从而增加可用于培训的数据。它尊重操作系统文件格式，以确保恶意软件在注射后仍将执行，并且不会改变其行为。我们复制了五种最先进的恶意软件分类方法来评估我们的注射方案：一种基于GIST+KNN，三个CNN变体和一种封闭式CNN。我们在公共数据集上进行了实验，并使用来自25个不同家庭的9,339个恶意软件样本进行了实验。我们的结果表明，恶意软件的大小增加了7％，导致恶意软件家庭分类的准确度下降了25％至40％。他们表明，自动恶意软件分类系统可能不像文献中最初报道的那样值得信赖。我们还使用修改后的麦芽麦克风以及原始恶核评估，以提高网络的鲁棒性，以防止上述攻击。结果表明，重新排序恶意软件部分和注入随机数据的组合可以改善分类的整体性能。代码可在https://github.com/adeilsonsilva/malware-injection中找到。
We investigate how to modify executable files to deceive malware classification systems. This work's main contribution is a methodology to inject bytes across a malware file randomly and use it both as an attack to decrease classification accuracy but also as a defensive method, augmenting the data available for training. It respects the operating system file format to make sure the malware will still execute after our injection and will not change its behavior. We reproduced five state-of-the-art malware classification approaches to evaluate our injection scheme: one based on GIST+KNN, three CNN variations and one Gated CNN. We performed our experiments on a public dataset with 9,339 malware samples from 25 different families. Our results show that a mere increase of 7% in the malware size causes an accuracy drop between 25% and 40% for malware family classification. They show that a automatic malware classification system may not be as trustworthy as initially reported in the literature. We also evaluate using modified malwares alongside the original ones to increase networks robustness against mentioned attacks. Results show that a combination of reordering malware sections and injecting random data can improve overall performance of the classification. Code available at https://github.com/adeilsonsilva/malware-injection.
