2026-10-29T21:49:10Zhttps://munzur-rdlopen.rawdatalibrary.net/oai/request

oai:munzur-rdlopen.rawdatalibrary.net:munzur/26942026-10-28T15:30:00Zopenairedrivercom_munzur_1col_munzur_2com_munzur_8col_munzur_154

Using network traffic analysis deep learning based Android malware detection; [Aǧ trafiǧi analizi ile derin öǧrenme tabanli Android kötücül yazilim tespiti]Utku, AnilAndroid malware detectiondeep learningmachine learningnetwork traffic analysisMobile devices, which are becoming more and more widespread today, have turned into hand-held computers thanks to the multimedia communication and applications. Today, the multimedia applications have been supported by traditional mobile phones. With their increasing functionality, mobile devices are used for many purposes such as enriched internet experience, financial transactions, access to social media platforms, sharing, music and video. Performing transactions where sensitive personal data transfers such as banking and shopping are carried out on mobile devices make mobile devices the target of attackers. In this study, a deep learning based malware detection system has been developed based on the interactions of mobile applications on the network. The developed LSTM-based deep learning model has been analysed comparatively with NB, RF, SVM, MLP, CNN, RNN and GRU using accuracy, precision, recall and F-1 metrics. The experimental results showed that the developed LSTM-based deep learning model is more successful in malware detection than others with 95% accuracy.Gazi Universitesi2022Articlehttps://rdlopen.rawdatalibrary.net/r/munzur/2694https://doi.org/10.17341/gazimmfd.9373741300-1884trJournal of the Faculty of Engineering and Architecture of Gazi Universityinfo:eu-repo/semantics/openAccessinfo:eu-repo/semantics/articlehttps://munzur-rdlopen.rawdatalibrary.net/tr/items/munzur/2694