Random Capsnet Forest Model for Imbalanced Malware Type Classification Task

dc.contributor.author Çayır, Aykut
dc.contributor.author Dağ, Hasan
dc.contributor.author Ünal, Uğur
dc.contributor.author Dağ, Hasan
dc.contributor.other Management Information Systems
dc.date.accessioned 2021-01-26T13:34:02Z
dc.date.available 2021-01-26T13:34:02Z
dc.date.issued 2021
dc.department Fakülteler, İşletme Fakültesi, Yönetim Bilişim Sistemleri Bölümü en_US
dc.description.abstract Behavior of malware varies depending the malware types, which affects the strategies of the system protection software. Many malware classification models, empowered by machine and/or deep learning, achieve superior accuracies for predicting malware types. Machine learning-based models need to do heavy feature engineering work, which affects the performance of the models greatly. On the other hand, deep learning-based models require less effort in feature engineering when compared to that of the machine learning-based models. However, traditional deep learning architectures components, such as max and average pooling, cause architecture to be more complex and the models to be more sensitive to data. The capsule network architectures, on the other hand, reduce the aforementioned complexities by eliminating the pooling components. Additionally, capsule network architectures based models are less sensitive to data, unlike the classical convolutional neural network architectures. This paper proposes an ensemble capsule network model based on the bootstrap aggregating technique. The proposed method is tested on two widely used, highly imbalanced datasets (Malimg and BIG2015), for which the-state-of-the-art results are well-known and can be used for comparison purposes. The proposed model achieves the highest F-Score, which is 0.9820, for the BIG2015 dataset and F-Score, which is 0.9661, for the Malimg dataset. Our model also reaches the-state-of-the-art, using 99.7% lower the number of trainable parameters than the best model in the literature. en_US
dc.identifier.citationcount 28
dc.identifier.doi 10.1016/j.cose.2020.102133 en_US
dc.identifier.issn 0167-4048 en_US
dc.identifier.issn 0167-4048
dc.identifier.scopus 2-s2.0-85098454089 en_US
dc.identifier.scopusquality Q1
dc.identifier.uri https://hdl.handle.net/20.500.12469/3737
dc.identifier.uri https://doi.org/10.1016/j.cose.2020.102133
dc.identifier.volume 102 en_US
dc.identifier.wosquality Q1
dc.institutionauthor Daǧ, Hasan en_US
dc.institutionauthor Çayır, Aykut en_US
dc.language.iso en en_US
dc.publisher Elsevier en_US
dc.relation.journal Computers and Security en_US
dc.relation.publicationcategory Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı en_US
dc.rights info:eu-repo/semantics/openAccess en_US
dc.scopus.citedbyCount 39
dc.subject Capsule networks en_US
dc.subject Deep learning en_US
dc.subject Ensemble model en_US
dc.subject Machine learning en_US
dc.subject Malware en_US
dc.title Random Capsnet Forest Model for Imbalanced Malware Type Classification Task en_US
dc.type Article en_US
dspace.entity.type Publication
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