Malware-Detection Using Transfer Learning of ResNet-50

Implementation of the article "Malicious Software Classification using Transfer Learning of ResNet-50 Deep Neural Network" under "classifier.ipynb". Achieved 98% Accuracy.
Malware detection using transfer learning from ResNet-50, with only 2 classes- benign and malicious, i.e- distinction between benign and malicious software.
Data:
- MalImg dataset- consists of 9,339 labeled malware binary images in 25 different malware classes.
- Benign dataset- created by us, consists of 7592 benign binary images.

Name		Name	Last commit message	Last commit date
Latest commit History 56 Commits
Saved_Plots		Saved_Plots
data		data
pre_processing		pre_processing
EDA_malware_benign.ipynb		EDA_malware_benign.ipynb
README.md		README.md
all_to_all_detector.ipynb		all_to_all_detector.ipynb
classifier.ipynb		classifier.ipynb
leave_one_out_detector.ipynb		leave_one_out_detector.ipynb

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