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  • Open Access

    ARTICLE

    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 - 13 December 2021

    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 >

  • Open Access

    ARTICLE

    Massive IoT Malware Classification Method Using Binary Lifting

    Hae-Seon Jeong1, Jin Kwak2,*

    Intelligent Automation & Soft Computing, Vol.32, No.1, pp. 467-481, 2022, DOI:10.32604/iasc.2022.021038 - 26 October 2021

    Abstract Owing to the development of next-generation network and data processing technologies, massive Internet of Things (IoT) devices are becoming hyperconnected. As a result, Linux malware is being created to attack such hyperconnected networks by exploiting security threats in IoT devices. To determine the potential threats of such Linux malware and respond effectively, malware classification through an analysis of the executed code is required; however, a limitation exists in that each heterogeneous architecture must be analyzed separately. However, the binary codes of a heterogeneous architecture can be translated to a high-level intermediate representation (IR) of the More >

  • Open Access

    ARTICLE

    Analysis of Feature Importance and Interpretation for Malware Classification

    Dong-Wook Kim1, Gun-Yoon Shin1, Myung-Mook Han2, *

    CMC-Computers, Materials & Continua, Vol.65, No.3, pp. 1891-1904, 2020, DOI:10.32604/cmc.2020.010933 - 16 September 2020

    Abstract This study was conducted to enable prompt classification of malware, which was becoming increasingly sophisticated. To do this, we analyzed the important features of malware and the relative importance of selected features according to a learning model to assess how those important features were identified. Initially, the analysis features were extracted using Cuckoo Sandbox, an open-source malware analysis tool, then the features were divided into five categories using the extracted information. The 804 extracted features were reduced by 70% after selecting only the most suitable ones for malware classification using a learning model-based feature selection More >

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