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All Journal ELKHA : Jurnal Teknik Elektro Semantik Proceeding SENDI_U Sinkron : Jurnal dan Penelitian Teknik Informatika JURNAL MEDIA INFORMATIKA BUDIDARMA Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control JOURNAL OF APPLIED INFORMATICS AND COMPUTING Jurnal Sisfokom (Sistem Informasi dan Komputer) Jurnal Abdimas PHB : Jurnal Pengabdian Masyarakat Progresif Humanis Brainstorming Building of Informatics, Technology and Science Jurnal Teknologi Dan Sistem Informasi Bisnis Jurnal Informatika dan Rekayasa Perangkat Lunak Abdimasku : Jurnal Pengabdian Masyarakat Journal of Intelligent Computing and Health Informatics (JICHI) Jurnal Teknik Informatika (JUTIF) Jurnal Informatika: Jurnal Pengembangan IT INOVTEK Polbeng - Seri Informatika
Fauzi Adi Rafrastara
Fakultas Ilmu Komputer, Universitas Dian Nuswantoro, Semarang 50131
Automotive Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Health Professions Industrial & Manufacturing Engineering Mechanical Engineering Medicine & Pharmacology Public Health Social Sciences Other

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Journal : Jurnal Teknik Informatika (JUTIF)

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OPTIMIZING ANDROID MALWARE DETECTION USING NEURAL NETWORKS AND FEATURE SELECTION METHOD Bintoro, Jevan; Rafrastara, Fauzi Adi; Latifah, Ines Aulia; Ghozi, Wildani; Yassin, Warusia
Jurnal Teknik Informatika (Jutif) Vol. 5 No. 6 (2024): JUTIF Volume 5, Number 6, Desember 2024
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2024.5.6.3898

Abstract

Malware poses a serious threat to Android security systems. In recent years, Android malware has rapidly evolved, employing obfuscation techniques such as polymorphic and metamorphic. Unfortunately, signature-based malware detection cannot identify modern variants of Android malware. This study aims to compare various feature selection methods and machine learning algorithms to identify the most effective and efficient combination for classifying Android malware. The dataset used in this research is the Drebin dataset. Four classification algorithms are used in this comparison: Naive Bayes, Logistic Regression, Neural Network, and Random Forest. The best-performing algorithm is then implemented in three different scenarios: without feature selection, with Information Gain, and with Chi-Squared (X²). In the latter two scenarios, the appropriate number of features was selected using the backward elimination method. Both feature selections achieved the same performance, but Information Gain required fewer features. The evaluation metrics used in this study include AUC, accuracy, F1-score, training time, and testing time. Measuring training and testing time benefits the model by making it more efficient, thus allowing for faster detection in real-world applications. The results show that the combination of the Information Gain feature selection method and the Neural Network algorithm achieves the highest performance, with an accuracy and F1-Score of 98.6%. Additionally, this combination achieves a training time of 81.135 seconds and a testing time of 1.095 seconds. Compared to the Neural Network algorithm without feature selection, this combination results in a 17.7597 % reduction in training time and a 57.9977 % reduction in testing time while maintaining the same performance values. This research contributes to improving the speed and accuracy of malware detection systems, enhancing mobile security.
IMPROVING MALWARE DETECTION USING INFORMATION GAIN AND ENSEMBLE MACHINE LEARNING Ramadhani, Arsabilla; Rafrastara, Fauzi Adi; Rosyada, Salma; Ghozi, Wildanil; Osman, Waleed Mahgoub
Jurnal Teknik Informatika (Jutif) Vol. 5 No. 6 (2024): JUTIF Volume 5, Number 6, Desember 2024
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2024.5.6.3903

Abstract

Malware attacks pose a serious threat to digital systems, potentially causing data and financial losses. The increasing complexity and diversity of malware attack techniques have made traditional detection methods ineffective, thus AI-based approaches are needed to improve the accuracy and efficiency of malware detection, especially for detecting modern malware that uses obfuscation techniques. This study addresses this issue by applying ensemble-based machine learning algorithms to enhance malware detection accuracy. The methodology used involves Random Forest, Gradient Boosting, XGBoost, and AdaBoost, with feature selection using Information Gain. Datasets from VirusTotal and VxHeaven, including both goodware and malware samples. The results show that Gradient Boosting, strengthened with Information Gain, achieved the highest accuracy of 99.1%, indicating a significant improvement in malware detection effectiveness. This study demonstrates that applying Information Gain to Gradient Boosting can improve malware detection accuracy while reducing computational requirements, contributing significantly to the optimization of digital security systems.
Co-Authors Abdollah, Mohd. Faizal Abdussalam Abdussalam, Abdussalam Adhitya Nugraha Adhitya Nugraha Ahmed, Foez Al Fahreza, Muhammad Daffa Amalia, Syafira Rosa Amiral, Afinzaki Anargya, Muhammad Alden Nayef Astuti, Yani Parti Bintoro, Jevan Catur Supriyanto Catur Supriyanto Catur Supriyanto Cinantya Paramita Cinantya Paramita Dewi Pergiwati Dwi Eko Waluyo Dwi Puji Prabowo Dwi Puji Prabowo, Dwi Puji Egia Rosi Subhiyakto, Egia Rosi Erlin Dolphina Etika Kartikadarma Firmansyah, Muhammad David Foez Ahmed Ghozi, Wildani Hayu Wikan Kinasih Heru Lestiawan Hussein, Jasim Nadheer Hussein, Jassim Nadheer Ifan Rizqa Ilma, Zidni Kencana, Lisdi Inu L. Budi Handoko Latifah, Ines Aulia Lisdi Inu Kencana Mahendra, Syafrie Naufal Megantara, Rama Aria Nohan, Rajendra Osman, Waleed Mahgoub Paramita, Cinantya Pergiwati, Dewi Pulung Nurtantio Andono Putra Hartanto, Octaviano Ryan Eka Ramadhan Rakhmat Sani Ramadhani, Arsabilla Ricardus Anggi Pramunendar Rosyada, Salma Saputra, Ardi Sudibyo, Usman T. Sutojo Tyas, Wahyu Suryaning Usman Sudibyo Widyatama, Thierry Wildanil Ghozi Yani Parti Astuti Yani Parti Astuti Yassin, Warusia