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Thakur, Shivam (2023) Malware Detection Using a Novel Ensemble Machine Learning Technique. Masters thesis, Dublin, National College of Ireland.
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Abstract
Malware is costing billions of dollars to organizations worldwide. The first line of defence against malware is Signature-Based Malware detection. This technique while great for initial detection has limitations that it works only against those malware whose signature is in the database. Therefore, there is a need for an Artificial Intelligence (AI) and Machine Learning (ML) model that can be trained on signatures and then can predict quickly and accurately whether a new signature can be classified as a safe file or malware.
In this research a novel ensemble model is presented that uses three AI models, Naïve Bayes, K-Nearest Neighbour and Logistic Regression. The accuracy of the model in predicting malware over the used dataset was 92.69%. and a run time of 91 seconds. However, this work concludes that KNN alone is the most suitable technique.
| Item Type: | Thesis (Masters) |
|---|---|
| Supervisors: | Name Email Sahni, Vikas UNSPECIFIED |
| Subjects: | Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science Q Science > QH Natural history > QH301 Biology > Methods of research. Technique. Experimental biology > Data processing. Bioinformatics > Artificial intelligence Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Artificial intelligence Q Science > QA Mathematics > Computer software > Computer Security T Technology > T Technology (General) > Information Technology > Computer software > Computer Security Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Machine learning |
| Divisions: | School of Computing > Master of Science in Cyber Security |
| Depositing User: | Tamara Malone |
| Date Deposited: | 06 Nov 2024 17:25 |
| Last Modified: | 06 Nov 2024 17:25 |
| URI: | https://norma.ncirl.ie/id/eprint/7156 |
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