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All Journal Perfecting a Video Game with Game Metrics Journal of Educational, Health and Community Psychology JUITA : Jurnal Informatika Khazanah Informatika: Jurnal Ilmu Komputer dan Informatika JUTEI (Jurnal Terapan Teknologi Informasi) ComTech: Computer, Mathematics and Engineering Applications Jurnal Leverage, Engagement, Empowerment of Community (LeECOM) Juisi (Jurnal Informatika dan Sistem Informasi) Jurnal Teknik Informatika (JUTIF) International Journal of Computer and Information System (IJCIS) Jurnal Pendidikan dan Pengabdian Masyarakat International Journal of Review Management Business and Entrepreneurship (RMBE)
Mychael Maoeretz Engel
Universitas Ciputra Surabaya
Agriculture, Biological Sciences & Forestry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Mathematics Public Health Social Sciences Other

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Journal : Khazanah Informatika: Jurnal Ilmu Komputer dan Informatika

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DETECTION OF CYBER MALWARE ATTACK BASED ON NETWORK TRAFFIC FEATURES USING NEURAL NETWORK Engel, Ventje Jeremias Lewi; Joshua, Evan; Engel, Mychael Maoeretz
Khazanah Informatika Vol. 6 No. 1 April 2020
Publisher : Universitas Muhammadiyah Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23917/khif.v6i1.8869

Abstract

Various techniques have been developed to detect cyber malware attacks, such as behavior based method which utilizes the analysis of permissions and system calls made by a process. However, this technique cannot handle the types of malware that continue to evolve. Therefore, an analysis of other suspicious activities ? namely network traffic or network traffic ? need to be conducted. Network traffic acts as a medium for sending information used by malware developers to communicate with malware infecting a victim's device. Malware analyzed in this study is divided into 3 classes, namely adware, general malware, and benign. The malware classification implements 79 features extracted from network traffic flow and an analysis of these features using a Neural Network that matches the characteristics of a time-series feature. The total flow of network traffic used is 442,240 data. The results showed that 15 main features selected based on literature studies resulted in F-measure 0.6404 with hidden neurons 12, learning rate 0.1, and epoch 300. As a comparison, the researchers chose 12 features based on the nature of the malware possessed, with the F-measure score of 0.666 with hidden neurons 12, learning rate 0.05, and epoch 300. This study found the importance of data normalization technique to ensure that no feature was far more dominant than other features. It was concluded that the analysis of network traffic features using Neural Network can be used to detect cyber malware attacks and more features does not imply better detection performance, but real-time malware detection is required for network traffic on IoT devices and smartphones.
Detection of Cyber Malware Attack Based on Network Traffic Features Using Neural Network Ventje Jeremias Lewi Engel; Evan Joshua; Mychael Maoeretz Engel
Khazanah Informatika Vol. 6 No. 1 April 2020
Publisher : Department of Informatics, Universitas Muhammadiyah Surakarta, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23917/khif.v6i1.8869

Abstract

Various techniques have been developed to detect cyber malware attacks, such as behavior based method which utilizes the analysis of permissions and system calls made by a process. However, this technique cannot handle the types of malware that continue to evolve. Therefore, an analysis of other suspicious activities – namely network traffic or network traffic – need to be conducted. Network traffic acts as a medium for sending information used by malware developers to communicate with malware infecting a victim's device. Malware analyzed in this study is divided into 3 classes, namely adware, general malware, and benign. The malware classification implements 79 features extracted from network traffic flow and an analysis of these features using a Neural Network that matches the characteristics of a time-series feature. The total flow of network traffic used is 442,240 data. The results showed that 15 main features selected based on literature studies resulted in F-measure 0.6404 with hidden neurons 12, learning rate 0.1, and epoch 300. As a comparison, the researchers chose 12 features based on the nature of the malware possessed, with the F-measure score of 0.666 with hidden neurons 12, learning rate 0.05, and epoch 300. This study found the importance of data normalization technique to ensure that no feature was far more dominant than other features. It was concluded that the analysis of network traffic features using Neural Network can be used to detect cyber malware attacks and more features does not imply better detection performance, but real-time malware detection is required for network traffic on IoT devices and smartphones.
Co-Authors Amanda Teonata Daniel Martomanggolo Wonohadidjojo Evan Joshua Evan Tanuwijaya Jenny Lukito Setiawan Jessica Christina Widhigdo Joshua, Evan Kennan Trevyn Zenjaya La Ode Arsad Sani Laura Mahendratta Tjahjono Lestari, Caecilia Citra Lisa Indriati Natanael Geraldo Santoso Nugroho, Agus Cahyo Reinaldo Lewis Lordianto Theresia Ratih Dewi Saputri Tony Antonio Ventje Jeremias Lewi Engel Ventje Jeremias Lewi Engel Yuwono Marta Dinata