Maldeb Dataset (doi:10.34820/FK2/HQYV4X)

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Maldeb Dataset

doi:10.34820/FK2/HQYV4X

Telkom University Dataverse

2024-03-28

1

ISMAIL, SETIA JULI IRZAL, 2024, "Maldeb Dataset", https://doi.org/10.34820/FK2/HQYV4X, Telkom University Dataverse, V1

Maldeb Dataset

Malware-benign Image Representation Dataset

doi:10.34820/FK2/HQYV4X

ISMAIL, SETIA JULI IRZAL (Institut Teknologi Bandung, Telkom University)

Debi Amalia Septiyani

Halimul Hakim Khairul

Dani Agung Prastiyo

Telkom University Dataverse

ISMAIL, SETIA JULI IRZAL

JULI IRZAL ISMAIL, SETIA

2024-03-28

Computer and Information Science, malware

malware

Malware-benign Image representation. The Dataset were collected from several malware repositories, including TekDefense, TheZoo, The Malware-Repo, Malware Database amd Malware Bazar. The benign samples were collected from Microsoft 10 and 11 system apps and several open source software repository including CNET, Sourceforge, FileForum, PortableFreeware. The samples were validated by scanning them using Virustotal Malware scanning services. The Samples underwent preprocessing by converting the malware binary into grayscale images following rules from Nataraj (2011). Nataraj Paper: https://vision.ece.ucsb.edu/research/signal-processing-malware-analysis. Maldeb Dataset is collected by Debi Amalia Septiyani and Halimul Hakim Khairul D. A. Septiyani, “Generating Grayscale and RGB Images dataset for windows PE malware using Gist Features extaction method,” Institut Teknologi Bandung, 2022, and Dani Agung Prastiyo, "Design and implementation of a machine learning-based malware classification system with an audio signal feature Analysis Approach," Institut Teknologi Bandung, 2023. The complete dataset can be accessed on this link https://ieee-dataport.org/documents/maldeb-dataset and https://github.com/julismail/Self-Supervised

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MalSSL - Self-Supervised Learning for Accurate and Label-efficient malware Classificaition

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