Abstract
Our project investigates the application of various machine learning techniques for malware detection. As malware engineers improve and release new variants, identifying whether a file is malicious becomes increasingly challenging. This malware evolution gives rise to concept drift, where changes in malware features degrade model performance, and also contribute to data scarcity due to limited availability of labelled samples. We explore methods to address these challenges using methods like explainable drift detection, synthetic data augmentation and adaptive malware classification.
Videos 1
Watch presentations, demos, and related content
Like, comment, and subscribe on YouTube to support the creator!
Gallery 1
Explore the visual story of this exhibit
Intelligent malware analysis