DeepSign: Deep Learning for Automatic Malware Signature Generation and Classification
2015 International Joint Conference on Neural Networks (IJCNN)(2017)
摘要
This paper presents a novel deep learning based method for automatic malware signature generation and classification. The method uses a deep belief network (DBN), implemented with a deep stack of denoising autoencoders, generating an invariant compact representation of the malware behavior. While conventional signature and token based methods for malware detection do not detect a majority of new variants for existing malware, the results presented in this paper show that signatures generated by the DBN allow for an accurate classification of new malware variants. Using a dataset containing hundreds of variants for several major malware families, our method achieves 98.6% classification accuracy using the signatures generated by the DBN. The presented method is completely agnostic to the type of malware behavior that is logged (e.g., API calls and their parameters, registry entries, websites and ports accessed, etc.), and can use any raw input from a sandbox to successfully train the deep neural network which is used to generate malware signatures.
更多查看译文
关键词
Deep Learning,Deep Belief Network,Autoencoders,Maiware,Automatic Signature Generation
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络