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dc.contributor.authorLytras, Miltiadis
dc.contributor.authorKwok Tai Chui
dc.contributor.authorPatricia Ordóñez de Pablos
dc.contributor.authorRyan Wen Liu
dc.contributor.authorChien-wen Shen
dc.date.accessioned2023-03-14T11:24:10Z
dc.date.available2023-03-14T11:24:10Z
dc.date.issuedJanuary 2022
dc.identifier.urihttp://hdl.handle.net/20.500.14131/665
dc.description.abstractSoftware has been the essential element to computers in today's digital era. Unfortunately, it has experienced challenges from various types of malware, which are designed for sabotage, criminal money-making, and information theft. To protect the gadgets from malware, numerous malware detection algorithms have been proposed. In the olden days there were shallow learning algorithms, and in recent years there are deep learning algorithms. With the availability of big data for training of model and affordable and high-performance computing services, deep learning has demonstrated its superiority in many smart city applications, in terms of accuracy, error rate, etc. This chapter intends to conduct a systematic review on the latest development of deep learning algorithms for malware detection. Some future research directions are suggested for further exploration.en_US
dc.publisherIGI Globalen_US
dc.subjectDeep Learningen_US
dc.subjectAdvanced Machine Learningen_US
dc.titleThe era of advanced machine learning and deep learning algorithms for malware detectionen_US
dc.source.booktitleAdvances in Malware and Data-Driven Network Securityen_US
dc.source.pages59-73en_US
dc.contributor.researcherExternal Collaborationen_US
dc.subject.KSAICTen_US
dc.source.indexScopusen_US
dc.source.indexWoSen_US
dc.contributor.departmentComputer Scienceen_US
dc.contributor.firstauthorKwok Tai Chui


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