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All Journal International Journal of Evaluation and Research in Education (IJERE) ComEngApp : Computer Engineering and Applications Journal Jurnal Ilmu Komputer dan Informasi Computer Engineering and Applications Journal (ComEngApp) Perfecting a Video Game with Game Metrics JUITA : Jurnal Informatika Proceeding of the Electrical Engineering Computer Science and Informatics Computer Engineering and Applications Journal (ComEngApp) Jurnal Informatika Upgris Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal Ilmiah Matrik Indonesian Journal of Information System JMM (Jurnal Masyarakat Mandiri) Martabe : Jurnal Pengabdian Kepada Masyarakat Jurdimas (Jurnal Pengabdian Kepada Masyarakat) Royal Jurnal Informatika Global Jurnal Ilmiah Binary STMIK Bina Nusantara Jaya Jurnal Abdimas Mandiri Reswara: Jurnal Pengabdian Kepada Masyarakat Journal of Computer Networks, Architecture and High Performance Computing Lumbung Inovasi: Jurnal Pengabdian Kepada Masyarakat Indonesian Community Journal International Journal of Advanced Science Computing and Engineering AnoaTIK: Jurnal Teknologi Informasi dan Komputer Jurnal INFOTEL
Ahmad Sanmorino
Universitas Indo Global Mandiri
Religion Agriculture, Biological Sciences & Forestry Arts Humanities Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Public Health Social Sciences Other

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Journal : JUITA : Jurnal Informatika

 1 
DDoS Attacks Detection Method Using Feature Importance and Support Vector Machine Ahmad Sanmorino; Rendra Gustriansyah; Juhaini Alie
JUITA : Jurnal Informatika JUITA Vol. 10 No. 2, November 2022
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (861.248 KB) | DOI: 10.30595/juita.v10i2.14939

Abstract

In this study, the author wants to prove the combination of feature importance and support vector machine relevant to detecting distributed denial-of-service attacks. A distributed denial-of-service attack is a very dangerous type of attack because it causes enormous losses to the victim server. The study begins with determining network traffic features, followed by collecting datasets. The author uses 1000 randomly selected network traffic datasets for the purposes of feature selection and modeling. In the next stage, feature importance is used to select relevant features as modeling inputs based on support vector machine algorithms. The modeling results were evaluated using a confusion matrix table. Based on the evaluation using the confusion matrix, the score for the recall is 93 percent, precision is 95 percent, and accuracy is 92 percent. The author also compares the proposed method to several other methods. The comparison results show the performance of the proposed method is at a fairly good level in detecting distributed denial-of-service attacks. We realized this result was influenced by many factors, so further studies are needed in the future.
Co-Authors Abdul Kholik Ade Fathurahman Agustina Heryati Amirah Aprianita Kartini Arvia Fildzah Suharmanto Dhamayanti Dhamayanti Dhamayanti Dhamayanti Ditho Hersilava Dwi Asa Verano Faradillah Faradillah Fery Antony Fitrah Karimah Imelda Saluza, Imelda Isabella Isabella Isabella, Isabella Juhaini Alie Marnisah, Luis Masroni Dedi Kiswanto Megat Norulazmi Megat Mohamed Noor Nazori Suhandi Nining Ariati Nining Ariati Rendra Gustriansyah Rudi Heriansyah Sanza Vittria Wulanda Sartika, Dewi Setiadi Yazid, Setiadi Shinta Puspasari Shinta Puspasari Sunardi, Hastha Suryati Suryati Tedy Setiawan Saputra Terttiaavini, Terttiaavini Terttiaavini, Terttiaavini Terttiavini Yulianti, Evi