View Proposal

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Proposer

Medhdhar Salem Ali Muthanna Al- Gaashani

Title

Intelligent Malware Detection and Classification Using Machine Learning

Goal

Description

Traditional signature-based antivirus can struggle with new and obfuscated malware. This project explores machine learning and deep learning methods for malware detection and classification using public datasets. Students preprocess the data, train and compare suitable baseline and advanced models, and evaluate them using accuracy, precision, recall, F1-score, and confusion matrices. Optionally, explainability methods and a simple prediction demo can be added.

Resources

Nataraj, L., Karthikeyan, S., Jacob, G., & Manjunath, B. S. (2011). Malware images: Visualization and automatic classification. In Proceedings of the 8th International Symposium on Visualization for Cyber Security (pp. 1–7). ACM.

Background

Url

Difficulty Level

Moderate

Ethical Approval

None

Number Of Students

1

Supervisor

Medhdhar Salem Ali Muthanna Al- Gaashani

Keywords

deep learning, cybersecurity, malware classification.

Degrees

Bachelor of Science in Computational Sciences and Software Engineering