Malware Detection Using Deep Learning

dc.contributor.advisorBhat, Tejas
dc.contributor.authorParkar, Saifallah Liyaqat
dc.date.accessioned2024-03-27T15:47:36Z
dc.date.available2024-03-27T15:47:36Z
dc.date.issued2024
dc.description.abstractSeveral models were implemented and evaluated in this work on deep learning-based malware detection. These models comprised Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), Graph Sample and Aggregated (GraphSAGE), Graph Convolutional Network (GCN), Hyper-parameter GCN, Graph Isomorphism Network, and Graph Attention Network. The Graph Isomorphism Network has the highest accuracy (97.58%), while the other models also had varied degrees of accuracy. The paper promotes cybersecurity by providing a comparative analysis of multiple deep-learning models for malware detection. The outcomes of this study may affect the development of more reliable and accurate malware detection systems. These findings indicate the promise of specialized graph-based neural networks, particularly Graph Isomorphism Networks. This work increases our understanding of deep learning applications in cybersecurity and stresses the need to select appropriate models for certain tasks, paving the way for more powerful malware detection tools.
dc.identifier.citationParkar, S. (2024).
dc.identifier.urihttps://hdl.handle.net/10788/4478
dc.language.isoen
dc.publisherDublin Business School
dc.rights.holderCopyright: the author
dc.rights.urihttp://esource.dbs.ie/copyright
dc.subjectMalware
dc.subjectMachine Learning
dc.subjectCybersecurity
dc.titleMalware Detection Using Deep Learning
dc.typeThesis
dc.type.degreelevelMSc
dc.type.degreenameMSc in Data Analytics
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