Enhancing Malware Detection Using Stacked BiLSTM with Attention Mechanism: A Deep Learning Approach

Masilamani, Srinivasan (2024) Enhancing Malware Detection Using Stacked BiLSTM with Attention Mechanism: A Deep Learning Approach. Masters thesis, Dublin, National College of Ireland.

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Abstract

The detection of malware offers quite a few challenges due to the ever-evolving nature of threats in the field of cybersecurity obviously. Traditional methods of malware detection methods already struggling to keep pace due to the rapidly changing landscape of malicious software. This study introduces a novel approach with the help of deep learning techniques which will enhance malware detection efficiency and address the prior work limitations like traditional methods which are already facing challenges. There are four models which are going to be performed in this project i.e., Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), Bidirectional Long Short-Term Memory (BiLSTM) networks and Stacked BiLSTM with an Attention Mechanism. These models are more effective in identifying complex patterns and dependencies found in Portable Executable (PE) files, which are frequently used as vectors for the spread of malware. Using an extensive dataset that includes both malware and benign PE files that are sourced from reliable repositories, this study guarantees a strong training framework. While this project tests all the four models, and after comparing the results, stacked BiLSTM with attention mechanism wins the accuracy margin. This study fills a critical gap in malware detection by leveraging advanced machine learning techniques to enhance cybersecurity defenses and mitigate the risks posed by evolving malware threats.

Item Type:	Thesis (Masters)
Supervisors:	Name Email Moldovan, Arghir-Nicolae UNSPECIFIED
Uncontrolled Keywords:	Malware detection; Deep learning; Stacked BiLSTM; Attention mechanism; Cybersecurity
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 T Technology > T Technology (General) > Information Technology > Computer software 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:	05 Jun 2025 10:19
Last Modified:	05 Jun 2025 10:19
URI:	https://norma.ncirl.ie/id/eprint/7748

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