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Detect Malware Types Data Set
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Abstract: Provide a short description of your data set (less than 200 characters).

Data Set Characteristics:  

Multivariate, Time-Series, Text

Number of Instances:

7107

Area:

Computer

Attribute Characteristics:

N/A

Number of Attributes:

280

Date Donated

2019-06-03

Associated Tasks:

Classification

Missing Values?

N/A

Number of Web Hits:

1783


Source:

Provide the names, email addresses, institutions, and other contact information of the donors and creators of the data set.


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:

Provide references to papers that have cited this data set in the past (if any).



Citation Request:

AF. Yazı, FÖ Çatak, E. Gül, Classification of Metamorphic Malware with Deep Learning (LSTM), IEEE Signal Processing and Applications Conference, 2019.

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


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