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Malware classification using deep learning methods

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dc.contributor.author Çakır, Buğra
dc.contributor.author Doğdu, Erdoğan
dc.date.accessioned 2022-06-15T12:47:33Z
dc.date.available 2022-06-15T12:47:33Z
dc.date.issued 2018
dc.identifier.citation Çakır, Buğra; Doğdu, Erdoğan (2018). "Malware classification using deep learning methods", Proceedings of the ACMSE 2018 Conference, 2018 Annual ACM Southeast Conference, ACMSE 2018; Richmond; 29 March 2018 through 31 March 2018. tr_TR
dc.identifier.isbn 9781450356961
dc.identifier.uri http://hdl.handle.net/20.500.12416/5629
dc.description.abstract Malware, short for Malicious Software, is growing continuously in numbers and sophistication as our digital world continuous to grow. It is a very serious problem and many efforts are devoted to malware detection in today’s cybersecurity world. Many machine learning algorithms are used for the automatic detection of malware in recent years. Most recently, deep learning is being used with better performance. Deep learning models are shown to work much better in the analysis of long sequences of system calls. In this paper a shallow deep learning-based feature extraction method (word2vec) is used for representing any given malware based on its opcodes. Gradient Boosting algorithm is used for the classification task. Then, k-fold cross-validation is used to validate the model performance without sacrificing a validation split. Evaluation results show up to 96% accuracy with limited sample data. © 2018 Association for Computing Machinery. tr_TR
dc.language.iso eng tr_TR
dc.relation.isversionof 10.1145/3190645.3190692 tr_TR
dc.rights info:eu-repo/semantics/closedAccess tr_TR
dc.subject Classification tr_TR
dc.subject Deep Learning tr_TR
dc.subject Machine Learning tr_TR
dc.subject Malware Detection tr_TR
dc.subject Supervised Learning tr_TR
dc.title Malware classification using deep learning methods tr_TR
dc.type conferenceObject tr_TR
dc.relation.journal Proceedings of the ACMSE 2018 Conference tr_TR
dc.contributor.department Çankaya Üniversitesi, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümü tr_TR


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