Betty Saridou, Joseph Rose, Stavros Shiaeles Basil Papadopoulos

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.

DOI Link: 
10.21227/vs0r-8s26
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Dataset Files

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[1] Betty Saridou, Joseph Rose, Stavros Shiaeles, Basil Papadopoulos, "48,240 Malware samples and Binary Visualisation Images for Machine Learning Anomaly Detection", IEEE Dataport, 2021. [Online]. Available: http://dx.doi.org/10.21227/vs0r-8s26. Accessed: Dec. 02, 2024.
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doi = {10.21227/vs0r-8s26},
url = {http://dx.doi.org/10.21227/vs0r-8s26},
author = {Betty Saridou; Joseph Rose; Stavros Shiaeles; Basil Papadopoulos },
publisher = {IEEE Dataport},
title = {48,240 Malware samples and Binary Visualisation Images for Machine Learning Anomaly Detection},
year = {2021} }
TY - DATA
T1 - 48,240 Malware samples and Binary Visualisation Images for Machine Learning Anomaly Detection
AU - Betty Saridou; Joseph Rose; Stavros Shiaeles; Basil Papadopoulos
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Betty Saridou, Joseph Rose, Stavros Shiaeles, Basil Papadopoulos. (2021). 48,240 Malware samples and Binary Visualisation Images for Machine Learning Anomaly Detection. IEEE Dataport. http://dx.doi.org/10.21227/vs0r-8s26
Betty Saridou, Joseph Rose, Stavros Shiaeles, Basil Papadopoulos, 2021. 48,240 Malware samples and Binary Visualisation Images for Machine Learning Anomaly Detection. Available at: http://dx.doi.org/10.21227/vs0r-8s26.
Betty Saridou, Joseph Rose, Stavros Shiaeles, Basil Papadopoulos. (2021). "48,240 Malware samples and Binary Visualisation Images for Machine Learning Anomaly Detection." Web.
1. Betty Saridou, Joseph Rose, Stavros Shiaeles, Basil Papadopoulos. 48,240 Malware samples and Binary Visualisation Images for Machine Learning Anomaly Detection [Internet]. IEEE Dataport; 2021. Available from : http://dx.doi.org/10.21227/vs0r-8s26
Betty Saridou, Joseph Rose, Stavros Shiaeles, Basil Papadopoulos. "48,240 Malware samples and Binary Visualisation Images for Machine Learning Anomaly Detection." doi: 10.21227/vs0r-8s26