摘要
With the development of information technology,malware threats to the industrial system have become an emergent issue,since various industrial infrastructures have been deeply integrated into our modern works and lives.To identify and classify new malware variants,different types of deep learning models have been widely explored recently.Generally,sufficient data is usually required to achieve a well-trained deep learning classifier with satisfactory generalization ability.However,in current practical applications,an ample supply of data is absent in most specific industrial malware detection scenarios.Transfer learning as an effective approach can be used to alleviate the influence of the small sample size problem.In addition,it can also reuse the knowledge from pretrained models,which is beneficial to the real-time requirement in industrial malware detection.In this paper,we investigate the transferable features learned by a 1D-convolutional network and evaluate our proposed methods on 6 transfer learning tasks.The experiment results show that 1D-convolutional architecture is effective to learn transferable features for malware classification,and indicate that transferring the first 2 layers of our proposed 1D-convolutional network is the most efficient way to reuse the learned features.
基金
the National Natural Science Foundation of China under Grants U1836106 and 81961138010
the Beijing Natural Science Foundation under Grants 19L2029 and M21032
the Scientific and Technological Innovation Foundation of Foshan under Grants BK20BF010 and BK21BF001
the Scientific and Technological Innovation Foundation of Shunde Graduate School,USTB,under Grant BK19BF006
by the Fundamental Research Funds for the University of Science and Technology Beijing under Grant FRF-BD-19-012A.
