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All Journal International Journal of Electrical and Computer Engineering Jurnal Ilmu Komputer Jurnal Edukasi dan Penelitian Informatika (JEPIN) JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control ILKOM Jurnal Ilmiah INTEK: Informatika dan Teknologi Informasi Jurnal Abdimas PHB : Jurnal Pengabdian Masyarakat Progresif Humanis Brainstorming Applied Technology and Computing Science Journal Infotekmesin Jurnal Sistem informasi dan informatika (SIMIKA) Journal of Innovation Information Technology and Application (JINITA) Madani : Indonesian Journal of Civil Society Journal of Informatics, Information System, Software Engineering and Applications (INISTA) Media Aplikom Jurnal Pengabdian Teknologi Tepat Guna Jurnal Nasional Teknik Elektro dan Teknologi Informasi
Muhammad Nur Faiz
Politeknik Negeri Cilacap
Religion Humanities Automotive Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Energy Engineering Languange, Linguistic, Communication & Media Library & Information Science Mechanical Engineering Public Health Social Sciences Other

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Journal : JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING

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Impact of Feature Selection Methods on Machine Learning-based for Detecting DDoS Attacks : Literature Review Muhammad Nur Faiz; Oman Somantri; Abdul Rohman Supriyono; Arif Wirawan Muhammad
JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Vol 5, No 2 (2022): Issues January 2022
Publisher : Universitas Medan Area

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31289/jite.v5i2.6112

Abstract

Cybersecurity attacks are becoming increasingly sophisticated and increasing with the development of technology so that they present threats to both the private and public sectors, especially Denial of Service (DoS) attacks and their variants which are often known as Distributed Denial of Service (DDoS). One way to minimize this attack is by using traditional mitigation solutions such as human-assisted network traffic analysis techniques but experiencing some limitations and performance problems. To overcome these limitations, Machine Learning (ML) has become one of the main techniques to enrich, complement and enhance the traditional security experience. The way ML works are based on the process of data collection, training and output. ML is influenced by several factors, one of which is feature engineering. In this study, we focus on the literature review of several recent studies which show that the feature selection process greatly impacts the level of accuracy of this ML. Datasets such as KDD, UNSW-NB15 and others also affect the level of accuracy of ML. Based on this literature review, this study can observe several feature engineering strategies with relevant impacts that can be chosen to improve ML solutions on DDoS attacks.
Co-Authors Abdul Fadlil Abdul Rohman Supriyono Achmad Zaki Yamani Agus Susanto Agus Susanto Andy Victor Pakpahan Anggita Dwi Fatimah Annas Setiawan Prabowo Anton Yudhana Ari Kristiningsih Arif Wirawan Muhammad Davito Rasendriya Rizqullah Putra Dwi Cita Septia Candra Erna Alimudin Fahmi Reza Ferdiansyah Ganjar Ndaru Ikhtiagung Gautama Wijaya Haeruddin Hani Hidayati Hani Hidayati Hendra Winata Hety Dwi Astuti Ifda Nurul Azroha Imam Riadi Isa Bahroni, Isa Khoeruddin Wittriansyah Laura Sari Linda Perdana Wanti Linda Perdana Wanti Lutfi Syafirullah Lutfi Syarifullah Nur Chasanah Nur Wachid Adi Prasetya Nur Wahyu Rahadi Oman Somantri Prih Diantono Abda`u Ratih Hafsarah Maharrani Robby Gustian Rusydi Umar Santi Purwaningrum Santi Purwaningrum Seppy Ayu Rachmawati Sidiq, Muhammad Fajar Sukma Aji Ummi Athiyah Wahyu Adi Prabowo