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检测恶意软件类型数据集

检测恶意软件类型数据集

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Computer Classification

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    README.md

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    Data Set Information:

    This study seeks to obtain data which will help to address machine learning based malware research gaps. The specific objective of this study is to build a benchmark dataset for Windows operating system API calls of various malware. This is the first study to undertake metamorphic malware to build sequential API calls. It is hoped that this research will contribute to a deeper understanding of how metamorphic malware change their behavior (i.e. API calls) by adding meaningless opcodes with their own dissembler/assembler parts. In our research, we have translated the families produced by each of the software into 8 main malware families: Trojan, Backdoor, Downloader, Worms, Spyware Adware, Dropper, Virus. Table 1 shows the number of malware belonging to malware families in our data set. As you can see in the table, the number of samples of other malware families except AdWare is quite close to each other. There is such a difference because we don't find too much of malware from the adware malware family.


    Attribute Information:

    Various Windows API calls


    Relevant Papers:

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    Citation Request:

    AF. Yaz?±, F?– ??atak, E. G??l, Classification of Metamorphic Malware with Deep Learning (LSTM), IEEE Signal Processing and Applications Conference, 2019.

    Catak, F?–., Yazi, AF., A Benchmark API Call Dataset for Windows PE Malware Classification, [Web Link], 2019.

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