Enhancing Malware Detection in PE Files Using Hybrid Ensemble Learning Techniques

Saini, Jatinder Singh (2024) Enhancing Malware Detection in PE Files Using Hybrid Ensemble Learning Techniques. Masters thesis, Dublin, National College of Ireland.

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Abstract

Malware has been a major issue in the online world for a decade, and its prevalence is expected to grow. As new technologies emerge, they inspire hackers to write harmful code and use it to steal information and do other malicious activities. Hackers mainly focus on Windows portable files as these files are carriers for malicious code. Machine learning methods have become a viable malware detection tool. However, malware developers are using these machine learning techniques to trick detection, which emphasizes the need for a more robust strategy. To enhance the detection approach, the study explores the capabilities of hybrid ensemble learning, focusing on tree-based algorithms, employing Gradient Boosting, Random Forest, AdaBoost, and a stacking classifier to strengthen the precision and as well as resilience of systems intended for malware detection. By combining the strengths of these diverse algorithms, this study intends to improve the efficacy and generalizability of malware recognition, offering a possibly promising approach to dealing with malware threats. The study findings have shown that the stacking classifier has achieved a 99% accuracy rate by combining the three algorithms and the application developed was able to make predictions on PE file samples more quickly, highlighting its potential for enhancing malware detection systems and making it an effective contribution in the cyber security domain.

Item Type:	Thesis (Masters)
Supervisors:	Name Email Lugones, Diego UNSPECIFIED
Uncontrolled Keywords:	Ensemble Learning; Machine Learning; Malware Detection
Subjects:	Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science 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:	Ciara O'Brien
Date Deposited:	03 Jun 2025 16:26
Last Modified:	03 Jun 2025 16:26
URI:	https://norma.ncirl.ie/id/eprint/7736

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