Malware Detection with Deep Learning

[Varunshiyam]

Is your feature request related to a problem? Please describe.

The increasing prevalence of malware on mobile devices, particularly Android, poses a significant threat to user security and privacy. Traditional malware detection methods often rely on signature-based approaches, which struggle to keep pace with the rapid evolution of malware and are easily evaded by new, unseen threats. This necessitates the development of more robust and adaptive techniques for accurate and efficient malware detection.

Describe the solution you'd like

This project demonstrates the use of deep learning for malware detection on Android devices. The dataset, containing behavioral characteristics of both benign and malware apps, is analyzed and preprocessed. Feature selection is performed to identify the most relevant attributes. A deep neural network is then constructed and trained using an Adam optimizer and sparse categorical cross-entropy loss. The model's performance is evaluated and visualized, showing high accuracy in classifying malware. Further training with an SGD optimizer is applied to potentially enhance the model's performance. This project highlights the effectiveness of deep learning in identifying malicious software based on behavioral patterns.

Additional context

[github-actions]

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[github-actions]

Hello @Varunshiyam! Your issue #701 has been closed. Thank you for your contribution!