Gaurav0502/malware-classification
Assessing 📊 the impact of class imbalance on model performance and convergence for malware byteplot image 🌌 classification
This project helps cybersecurity researchers and malware analysts understand how well different machine learning models can identify various types of malware from visual representations (byteplot images). It takes in malware image datasets with varying levels of class imbalance and outputs a comparison of how different state-of-the-art neural networks perform and converge under these conditions. The ideal user is a cybersecurity professional or researcher focused on improving automated malware detection.
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Use this if you need to evaluate and compare the effectiveness of different deep learning models for classifying malware based on their byteplot images, especially when dealing with datasets where some malware types are much rarer than others.
Not ideal if you are looking for a plug-and-play malware detection tool, or if your primary interest is in non-image-based malware analysis techniques.
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Last pushed
Oct 09, 2023
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