Federated Learning Approach for Vision-based Android Malware Family Classification using CNN
This repository houses a scientific report focused on the classification of Android malware using Federated Learning (FL). The study delves into the challenges posed by evolving cyber threats on Android platforms, offering insights into system vulnerabilities and proposing innovative solutions for timely detection and classification.
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Android Malware Classification: The report explores the categorization of Android malware variants, providing nuanced insights into system vulnerabilities for prioritized mitigation strategies.
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Image-Based Classification: A lightweight system is introduced, transforming APK files into RGB images for classification using a Convolutional Neural Network (CNN).
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Federated Learning Approach: Addressing privacy concerns, the study adopts Federated Learning (FL), leveraging Open Federated Learning (OpenFL) for decentralized collaboration on a machine learning model.
The report acknowledges current limitations, such as the need for a more comprehensive evaluation of FL scalability, exploration of diverse scenarios, and optimization of the modified FedAvg algorithm for improved performance.
- /FLvsCL_5rounds.ipynb: Includes the federation and decentralized learning over 5 rounds
- /FLvsCL_3rounds.ipynb: Includes the federation and decentralized learning over 3 rounds
- /APK_to_image.ipynb: Includes the demo for the conversion of APK files to 64x64 RGB images
We use the CICMalDroid2020 Dataset for this study.