DroidMalwareDetector: A novel Android malware detection framework based on convolutional neural network

Basit Öğe Kaydı
dc.authoridKabakuş, Abdullah Talha/0000-0003-2181-4292
dc.authorwosidKabakuş, Abdullah Talha/J-8361-2019
dc.contributor.authorKabakuş, Abdullah Talha
dc.date.accessioned2023-07-26T11:50:05Z
dc.date.available2023-07-26T11:50:05Z
dc.date.issued2022
dc.departmentDÜ, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümüen_US
dc.description.abstractSmartphones have become an integral part of our daily lives thanks to numerous reasons. While benefitting from what they offer, it is critical to be aware of the existence of malware in the Android ecosystem and be away from them. To this end, an end-to-end and highly effective Android malware detection framework based on CNN, namely, DroidMalwareDetector, was proposed within this study. Unlike most of the related work, DroidMalwar-eDetector was specifically designed to (i) automate feature extraction and selection, (ii) propose a novel CNN that operates with 1-dimensional data, and (iii) use intents and API calls alongside the widely used permissions to perform comprehensive malware analysis. The proposed framework was trained and evaluated on the con-structed dataset, which consisted of 14,386 apps from the de-facto standard datasets. The proposed framework's efficiency in terms of distinguishing malware from benign apps was revealed thanks to the conducted experi-ments. According to the experimental result, the accuracy of the proposed framework was calculated as high as 0.9, which was higher than the accuracy values obtained from a wide range of machine learning algorithms. The insights which were gained through the conducted experiments were revealed as another contribution to the research field.en_US
dc.identifier.doi10.1016/j.eswa.2022.117833
dc.identifier.issn0957-4174
dc.identifier.issn1873-6793
dc.identifier.scopus2-s2.0-85132320861en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://doi.org/10.1016/j.eswa.2022.117833
dc.identifier.urihttps://hdl.handle.net/20.500.12684/12228
dc.identifier.volume206en_US
dc.identifier.wosWOS:000822826900006en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.institutionauthorKabakuş, Abdullah Talha
dc.language.isoenen_US
dc.publisherPergamon-Elsevier Science Ltden_US
dc.relation.ispartofExpert Systems With Applicationsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.snmz$2023V1Guncelleme$en_US
dc.subjectAndroid; Android Malware Detection; Deep Neural Network; Convolutional Neural Network; Mobile Securityen_US
dc.subjectThreatsen_US
dc.titleDroidMalwareDetector: A novel Android malware detection framework based on convolutional neural networken_US
dc.typeArticleen_US

Dosyalar

Orijinal paket
Listeleniyor 1 - 1 / 1
Küçük Resim Yok
İsim:
12228.pdf
Boyut:
2.39 MB
Biçim:
Adobe Portable Document Format
Açıklama:
Tam Metin / Full Text
İndir

Koleksiyon

WoS İndeksli Yayınlar Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu