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    Transferable Features from 1D-Convolutional Network for Industrial Malware Classification

    Liwei Wang1,2,3, Jiankun Sun1,2,3, Xiong Luo1,2,3,*, Xi Yang4

    CMES-Computer Modeling in Engineering & Sciences, Vol.130, No.2, pp. 1003-1016, 2022, DOI:10.32604/cmes.2022.018492

    Abstract 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 More >

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