基于基因视角的恶意代码同源性判定（英文）
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摘要
恶意代码同源性判定对攻击事件溯源、应急响应方案处置以及事件发展趋势预测有重要作用。目前,恶意代码同源性判定以人工分析为主,效率较低,对安全事件的爆发无法快速响应。因此,提出一种新的从基因视角分析的恶意代码同源性判定方法。恶意代码基因由表示家族同源性的子图组成。通过筛选关键应用程序接口和利用社团划分算法,从函数依赖图中提取关键子图作为恶意代码基因。然后,设计一种频繁子图挖掘算法发现恶意代码家族的共有基因,并对基因编码。最后,利用家族共有基因指导恶意代码同源性判定。对公开数据集的分类和实验结果表明,分类准确率达97%,且效率较高。
Malware homology identification is important in attacking event tracing, emergency response scheme generation, and event trend prediction. Current malware homology identification methods still rely on manual analysis, which is inefficient and cannot respond quickly to the outbreak of attack events. In response to these problems, we propose a new malware homology identification method from a gene perspective. A malware gene is represented by the subgraph, which can describe the homology of malware families. We extract the key subgraph from the function dependency graph as the malware gene by selecting the key application programming interface(API) and using the community partition algorithm. Then, we encode the gene and design a frequent subgraph mining algorithm to find the common genes between malware families. Finally, we use the family genes to guide the identification of malware based on homology. We evaluate our method with a public dataset, and the experiment results show that the accuracy of malware classification reaches 97% with high efficiency.
Malware homology identification is important in attacking event tracing, emergency response scheme generation, and event trend prediction. Current malware homology identification methods still rely on manual analysis, which is inefficient and cannot respond quickly to the outbreak of attack events. In response to these problems, we propose a new malware homology identification method from a gene perspective. A malware gene is represented by the subgraph, which can describe the homology of malware families. We extract the key subgraph from the function dependency graph as the malware gene by selecting the key application programming interface(API) and using the community partition algorithm. Then, we encode the gene and design a frequent subgraph mining algorithm to find the common genes between malware families. Finally, we use the family genes to guide the identification of malware based on homology. We evaluate our method with a public dataset, and the experiment results show that the accuracy of malware classification reaches 97% with high efficiency.
引文
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Defferrard M,Bresson X,Vandergheynst P,2016.Convolutional neural networks on graphs with fast localized spectral filtering.Conf and Workshop on Neural Information Processing Systems,p.3837-3845.
Drew J,Moore T,Hahsler M,2016.Polymorphic malware detection using sequence classification methods.Security and Privacy Workshops,p.81-87.https://doi.org/10.1109/SPW.2016.30
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Kinable J,Kostakis O,2011.Malware classification based on call graph clustering.J Comput Virol,7(4):233-245.https://doi.org/10.1007/s11416-011-0151-y
Kipf TN,Welling M,2016.Semi-supervised classification with graph convolutional networks.https://arxiv.org/abs/1609.02907?context=cs
Kirat D,Vigna G,2015.MalGene:automatic extraction of malware analysis evasion signature.Proc 22nd ACMSIGSAC Conf on Computer and Communications Security,p.769-780.https://doi.org/10.1145/2810103.2813642
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Wang XZ,Liu JW,Chen XE,2015.Microsoft Malware Classification Challenge(Big 2015)first place team:say no to overfitting.https://github.com/xiaozhouwang/kaggle_Microsoft_Mal ware/blob/master/Saynotooverfitting.pdf[Accessed on Nov.2,2015].
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Yu B,Fang Y,Yang Q,et al.,2018.A survey of malware behavior description and analysis.Front Inform Technol Electron Eng,19(5):583-603.https://doi.org/10.1631/FITEE.1601745