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All Journal Jurnal Gamma IPTEK The Journal of Engineering TEKNOLOGI: Jurnal Ilmiah Sistem Informasi Jurnal Inovasi Teknologi Pendidikan Jurnal Edukasi dan Penelitian Informatika (JEPIN) Infotech Journal Register: Jurnal Ilmiah Teknologi Sistem Informasi Sistemasi: Jurnal Sistem Informasi Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Jurnal Informatika dan Multimedia Information Management For Educators And Professionals (IMBI) Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control INOVTEK Polbeng - Seri Informatika JITK (Jurnal Ilmu Pengetahuan dan Komputer) J-Dinamika: Jurnal Pengabdian Kepada Masyarakat Applied Technology and Computing Science Journal Prosiding Seminar Nasional Multidisiplin Abdi: Jurnal Pengabdian dan Pemberdayaan Masyarakat Jurnal ABDINUS : Jurnal Pengabdian Nusantara Jurnal Teknik Informatika C.I.T. Medicom Pelita Masyarakat Community Empowerment Jurnal Nasional Teknik Elektro dan Teknologi Informasi Jurnal Pengabdian pada Masyarakat Ilmu Pengetahuan dan Teknologi Terintegrasi Jurnal Informatika dan Multimedia Journal of Applied Business and Technology Jurnal Pengabdian Masyarakat Bangsa Information Technology Education Journal Prosiding Seminar Nasional Pengabdian Kepada Masyarakat: Peduli Masyarakat Journal of Information Technology and Cyber Security JURDIASRA
Abidatul Izzah
Politeknik Negeri Malang
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Journal : Journal of Information Technology and Cyber Security

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Supervised Learning Methods Comparison for Android Malware Detection Based on System Calls Referring to ARM (32-bit/EABI) Table Alhamri, Rinanza Zulmy; Cinderatama, Toga Aldila; Eliyen, Kunti; Izzah, Abidatul
Journal of Information Technology and Cyber Security Vol. 2 No. 1 (2024): January
Publisher : Department of Information Systems and Technology, Faculty of Intelligent Electrical and Informatics Technology, Universitas 17 Agustus 1945 Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30996/jitcs.10511

Abstract

Android malware detection research is a topic that is still being developed. From all the detection techniques developed, dynamic analysis methods have become interesting because they trace the suspect application system calls. Based on the system calls, by utilizing machine learning, the suspect application can be classified as malware or benign. Comparing the machine learning methods is im-portant to determine what method is best to support malware detection. This article aims to explain more clearly and simply the way to conduct Android malware detection based on system calls step by step using classification. Furthermore, it presents the system calls sequence conversion referring to the arm(32-bit/EABI) table, which has 398 system calls (0-397) as features. It will provide a compari-son of several supervised machine-learning methods for classifying Android applications. This initial research is part of the other research that has the purpose of developing a malware detection system based on an Android application. This research can be used to develop the best machine learning to classify malware applications using a Support Vector Machine (SVM), Decision Tree (DT), K-Nearest Neighbour (KNN), and Naive Bayes (NB). The result can be concluded that the KNN method has the lowest performance in detecting Android malware apps, with an accuracy of only 0.50. In comparison, the NB method has an accuracy of only 0,69. SVM and DT models have similar accuracy and recall results of 0.79 and 0.75, respectively, but DT obtained higher precision and scores of 0.83 and 0.76, respectively. Although in this study, the classification performance of DT is better than SVM, based on comparison with the results of previous research, SVM is a suitable method for Android malware de-tection based on system calls. It is proven by the results of research comparisons that the SVM method is always the method with the highest accuracy score among other methods. For the next research, the SVM method can be used to develop a malware detection system for Android applications.
Co-Authors Agata Iwan Candra Agung Nugroho, Benni Agustono Heriadi Agustono Heriadi, Agustono Ahmad Athoillah Saifi Ahmad Dony Mutiara Bahtiar Andarista, Felya Renanda Fergi Andika Kurnia Adi Pradana Andika Kurnia Adi Pradana, Andika Kurnia Arrie Kurniawardhani Asmoro, Wiwiek Kusumaning Aullia, Fikha Rizky Benni A. Nugroho Benni Agung Nugroho Benni Agung Nugroho Benni Agung Nugroho Benni Agung Nugroho Benni Agung Nugroho, Benni Agung Dani Mahbubatul J, Dani Mahbubatul Daniel Swanjaya Devina Rosa Hendarti Diani Mahbubatul J Dion Yanuarmawan Eliyen, Kunti Ellya Nurfarida Ellya Nurfarida Ellya Nurfarida Ellya Nurfarida Ellya Nurfarida, Ellya Fadelis Sukya Fery S. Efendi, Fery S. Fitra Abdurrachman Bachtiar Fitria Nur Hamidah, Fitria Nur Habibi, Isac Ilham Hadi Rahmad Hanafia Rahma Hanafia Rahma, Hanafia Hendy Kunti Eliyen Kunti Eliyen Kunti Eliyen Kusuma, Selvia F. Laylatul, Nur M Syaifulloh Annur, M Syaifulloh M. Syaifulloh Annur mukhamad masrur Mustika Mentari Nila Nurlina Nur Hayatin Nuru Aini Ratna Widyastuti Ratna Widyastuti Ratna Widyastuti Ratna Widyastuti Ratna Widyastuti Ratna Widyastuti, Ratna Rinanza Zulmy Alhamri, Rinanza Zulmy Riskitasari, Septyana Riswan Eko Wahyu Susanto Riswan Eko Wahyu Susanto, Riswan Eko Wahyu Saiful Arif Selvia Ferdiana Kusuma Selvia Ferdiana Kusuma Setyowati, Diah Eka Shofiya Syidada Siti Mutrofin SUDARMAJI SUDARMAJI Toga Aldila Cinderatama Wahyu, Mujahid Wayan F. Mahmudy Wendy Adnan Febianto Wendy Adnan Febianto, Wendy Adnan Wiwiek Kusumaning Asmoro Yanuarmawan, Dion Yohan Bakhtiar Zulfa Khalida