48,240 Malware samples and Binary Visualisation Images for Machine Learning Anomaly Detection

Creator: Joseph Rose
License: https://creativecommons.org/licenses/by/4.0/

Citation Author(s):: Betty Saridou (Democritus University of Thrace)

Joseph Rose (The University of Portsmouth)

Stavros Shiaeles (The University of Portsmouth)

Basil Papadopoulos (Democritus University of Thrace)
Submitted by:: Joseph Rose
Last updated:: Wed, 06/02/2021 - 23:00
DOI:: 10.21227/vs0r-8s26
Data Format:: *.jpg(ZIP)

.jpeg(ZIP)

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Categories:

Keywords:

machine learning; malware detection; computer security; dynamic analysis

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Abstract

Dataset including over 40,000 generated images of malicious binaries for malware classification in machine learning as outlined in NARAD - A Novel Auto-learn Real-time Fuzzy Machine Learning Anomaly Detection and Classification System.

Instructions:

Images used for classification of malware using fuzzy logic in binary visulisaiton.

Mirsat Yesiltepe Fri, 05/14/2021 - 18:30 Permalink

Sriram C Sat, 05/29/2021 - 11:49 Permalink

Apologies. We had an issue uploading the datasets on IEEE due to the large file sizes. Now you should be able to download them.

Joseph Rose Wed, 06/02/2021 - 22:56 Permalink

Hi Authors, Could you explain what is the difference between Malware files and Malicious files? Do you have any elaborated description the dataset? Thanks

Tarek Gaber Tue, 06/08/2021 - 21:53 Permalink

Can I have access?

Silvia Niecko Thu, 03/24/2022 - 11:35 Permalink

Please could I have access.

Adam Azam Thu, 05/05/2022 - 20:34 Permalink

can i have free access ? bruzzese.953247@studenti.uniroma1.it

roberto bruzzese Mon, 05/30/2022 - 11:54 Permalink

Please, provide the 40,000 generated images of malicious binaries for malware dataset

Saddam Khan Mon, 09/25/2023 - 06:46 Permalink

Please, provide the 40,000 generated images of malicious binaries for malware dataset

Om Shankar Sat, 09/14/2024 - 14:33 Permalink