A Deep Learning Approach to Malicious Software Detection: Combining MLP and GRU

Mohan, Ann (2024) A Deep Learning Approach to Malicious Software Detection: Combining MLP and GRU. Masters thesis, Dublin, National College of Ireland.

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Abstract

The paper introduces the MLP-GRU model for detecting malware from two aspects, the MLP for network statics and the GRU for sequences analysis. The purpose is to increase the number of detections, minimize false positives, and increase workability for real-time and mass detection. Through the model, 99.4 % of classification was made, faster training particularly for big data, and better detection than existing models such as – Decision trees, SVM, and CNN. Two criteria, requirements and class imbalance, are met while building the model. For future work, the focus should be made on scalability, the training of the algorithm for optimization, as well as the application of transfer learning and automated feature engineering.

Item Type:	Thesis (Masters)
Supervisors:	Name Email Salahuddin, Jawad UNSPECIFIED
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:	23 Jul 2025 15:26
Last Modified:	23 Jul 2025 15:26
URI:	https://norma.ncirl.ie/id/eprint/8228

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