Malware Detection System using Machine Learning & Deep Learning Technique

dc.contributor.authorKarim, Miskatul
dc.contributor.authorHossain, Md Jubair
dc.date.accessioned2024-03-30T05:21:21Z
dc.date.available2024-03-30T05:21:21Z
dc.date.issued2023-07
dc.descriptionThis Dissertation is Submitted in Fulfillmentof the Requirements for the Degree of Bachelor of Science (B.Sc.) in Computer Science and Engineering (CSE) Spring-2023en_US
dc.description.abstractThese days, with the amount and variety of malware increasing at an exponential rate, it is imperative to use innovative methods for quickly and precisely detecting this dangerous software. The fast velocity at which malware spreads makes manual heuristic inspections of malware analyses inefficient for both discovering new malware and keeping up with it. Machine-learning techniques have therefore become increasingly popular. By automating the examination of static and dynamic studies, these methods can classify unknown malware according to how close it is to recognized families and combine malware that exhibits similar behavior. While data mining and machine learning approaches have been applied in the past, this paper demonstrates how deep learning networks can improve accuracy even further. By building neural networks that have more potentially varied.en_US
dc.identifier.urihttp://dspace.iiuc.ac.bd:8080/xmlui/handle/123456789/8141
dc.language.isoenen_US
dc.publisherInternational Islamic University Chittagongen_US
dc.subjectMalwareen_US
dc.subjectHeuristic inspectionen_US
dc.subjectMachine learningen_US
dc.titleMalware Detection System using Machine Learning & Deep Learning Techniqueen_US
dc.typeThesisen_US

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