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首页> 外文会议>International conference on neural information processing;Annual conference of Asia-Pacific Neural Network Society >A Novel Image-Based Malware Classification Model Using Deep Learning
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A Novel Image-Based Malware Classification Model Using Deep Learning

机译：基于深度学习的新型基于图像的恶意软件分类模型

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摘要

Nowadays, the vast volume of data which needs to be evaluated potentially malicious is becoming one of the major challenges of antivirus products. In this paper, we propose a novel image-based malware classification model using deep learning to counter large-scale malware analysis. The model includes a malware embedding method called Yonglmage which maps instruction-level information and disassembly metadata generated by IDA disassembler tool into an image vector, and a deep neural network named malVecNet which has simpler structure and faster convergence rate. Our proposed Yonglmage converts malware analysis tasks into image classification problems, which do not rely on domain knowledge and complex feature extraction. Meanwhile, we use the thought of sentence-level classification in Natural Language Processing to establish and optimize our malVecNet. Compared to previous work, malVecNet has better theoretical interpretability and can be trained more effectively. We use 10-fold cross-validation on Microsoft malware classification challenge dataset to evaluate our model. The results demonstrate that our model can achieve 99.49% accuracy with 0.022 log loss. Although our scheme is less precise than the winner's, it makes an orders-of-magnitude performance boost. Compared with other related work, our model also outperforms most of them.
机译：如今，需要评估潜在恶意的大量数据正成为防病毒产品的主要挑战之一。在本文中，我们提出了一种使用深度学习来应对大规模恶意软件分析的基于图像的新型恶意软件分类模型。该模型包括一种称为Yonglmage的恶意软件嵌入方法，该方法将指令级信息和IDA拆装工具生成的拆装元数据映射到图像矢量中，以及一个名为malVecNet的深度神经网络，该网络具有更简单的结构和更快的收敛速度。我们提出的Yonglmage将恶意软件分析任务转换为图像分类问题，这些问题不依赖于领域知识和复杂的特征提取。同时，我们使用自然语言处理中句子级别分类的思想来建立和优化我们的malVecNet。与以前的工作相比，malVecNet具有更好的理论解释性，并且可以得到更有效的培训。我们对Microsoft恶意软件分类挑战数据集使用10倍交叉验证来评估我们的模型。结果表明，我们的模型可以实现99.49％的精度，而log损失为0.022。尽管我们的方案不如优胜者的方案精确，但它的性能却提高了一个数量级。与其他相关工作相比，我们的模型也表现出色。

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