Malware Detection Framework Using Hybrid Deep Learning Algorithms

Khan, Ayaan (2023) Malware Detection Framework Using Hybrid Deep Learning Algorithms. Masters thesis, Dublin, National College of Ireland.

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Abstract

As the complexity and stealth of malware approaches continue to advance, conventional signature- based detection technologies have challenges in keeping up with these developments. Considering this, scholars and professionals have resorted to employing sophisticated machine learning methodologies, including deep learning, to augment the precision of malware identification.

During the data preparation stage, the initial binary data is converted into appropriate input formats that are compatible with deep learning models. The process of feature extraction includes the retrieval of static information, such as opcode sequences, as well as dynamic features, such as system call sequences, from samples of malicious software. The classification step ultimately utilizes an ensemble methodology for decision-making, wherein the collective outputs of distinct deep learning models are amalgamated to formulate a conclusive forecast.

The initial findings indicate that the utilization of hybrid deep learning techniques in the Malware Detection Framework leads to enhanced detection performance in comparison to using individual models. The utilization of the ensemble technique significantly improves the overall resilience of the detection system, efficiently discerning both established and emerging malware variations.

Item Type:	Thesis (Masters)
Supervisors:	Name Email Pantridge, Michael UNSPECIFIED
Uncontrolled Keywords:	Malware detection; deep learning; hybrid models
Subjects:	Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science Q Science > QA Mathematics > Algebra > Algorithms > Computer algorithms 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:	22 Oct 2024 15:08
Last Modified:	22 Oct 2024 15:08
URI:	https://norma.ncirl.ie/id/eprint/7128

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