static analysis

Numerous studies have focused on exploring Android malware in recent years, covering areas such as malware detection and application analysis. As a result, there is a pressing need for a reliable and scalable malware dataset to support the development and evaluation of effective malware studies. Although several benchmarks for Android malware datasets are widely used in research, they have significant limitations. Firstly, many of these datasets are outdated and do not capture current malware trends. Additionally, some have become obsolete or inaccessible, limiting their usefulness.

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Whole-program analysis is an essential technique that enables advanced compiler

optimizations. An important example of such a method is points-to analysis used

by ahead-of-time (AOT) compilers to discover program elements (classes, methods,

fields) that may be used on at least one program path during the run of the

program and hence need to be compiled. GraalVM Native Image uses a points-to

analysis to optimize Java applications, which is a time-consuming step of the

build. We explore how much the analysis time can be improved by replacing the

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This dataset is part of my PhD research on malware detection and classification using Deep Learning. It contains static analysis data: Top-1000 imported functions extracted from the 'pe_imports' elements of Cuckoo Sandbox reports. PE malware examples were downloaded from virusshare.com. PE goodware examples were downloaded from portableapps.com and from Windows 7 x86 directories.

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This dataset is part of my PhD research on malware detection and classification using Deep Learning. It contains static analysis data: Raw PE byte stream rescaled to a 32 x 32 greyscale image using the Nearest Neighbor Interpolation algorithm and then flattened to a 1024 bytes vector. PE malware examples were downloaded from virusshare.com. PE goodware examples were downloaded from portableapps.com and from Windows 7 x86 directories.

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This dataset is part of my PhD research on malware detection and classification using Deep Learning. It contains static analysis data (PE Section Headers of the .text, .code and CODE sections) extracted from the 'pe_sections' elements of Cuckoo Sandbox reports. PE malware examples were downloaded from virusshare.com. PE goodware examples were downloaded from portableapps.com and from Windows 7 x86 directories.

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The whole data set will be published after the acceptance of our paper via the same url as shown in the paper.

 

When using PackageRank software to analyze our data set, please do not change the name of the .net files.

 

The .net file has the following format:

Node count: *Vertices count

 

Node List:

    number "node name"

EX:   1    "org.apache.tools.ant.taskdefs.optional.sitraka"

 

Arc List:

node1 node2 weight

EX: 1 2 3

Meaning: from node 1 to node 2 with weight 3

 

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