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All Journal Jurnal Simetris Jurnal Edik Informatika : Penelitian Bidang Komputer Sains dan Pendidikan Informatika Jurnal Mirai Management JOIV : International Journal on Informatics Visualization JEEMECS (Journal of Electrical Engineering, Mechatronic and Computer Science) JURNAL ILMIAH INFORMATIKA Journal Information System Development Batoboh : Jurnal Pengabdian Pada Masyarakat IJISTECH (International Journal Of Information System & Technology) Jurnal Tekinkom (Teknik Informasi dan Komputer) IJISTECH Jurnal Puan Indonesia Jurnal Sains Informatika Terapan (JSIT) JUPADAI : Jurnal Pengabdian Kepada Masyarakat JURSIMA Jurnal Sistem Informasi dan Manajemen Computer & Science Industrial Engineering Journal Prosiding Seminar Nasional Ilmu Sosial dan Teknologi (SNISTEK)
Sitohang, Sunarsan
Universitas Putera Batam
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Journal : JOIV : International Journal on Informatics Visualization

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Feature Selection to Enhance DDoS Detection Using Hybrid N-Gram Heuristic Techniques Andi Maslan; Kamaruddin Malik Bin Mohamad; Abdul Hamid; Hotma Pangaribuan; Sunarsan Sitohang
JOIV : International Journal on Informatics Visualization Vol 7, No 3 (2023)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.7.3.1533

Abstract

Various forms of distributed denial of service (DDoS) assault systems and servers, including traffic overload, request overload, and website breakdowns. Heuristic-based DDoS attack detection is a combination of anomaly-based and pattern-based methods, and it is one of three DDoS attack detection techniques available. The pattern-based method compares a sequence of data packets sent across a computer network using a set of criteria. However, it cannot identify modern assault types, and anomaly-based methods take advantage of the habits that occur in a system. However, this method is difficult to apply because the accuracy is still low, and the false positives are relatively high. Therefore, this study proposes feature selection based on Hybrid N-Gram Heuristic Techniques. The research starts with the conversion process, package extract, and hex payload analysis, focusing on the HTTP protocol. The results show the Hybrid N-Gram Heuristic-based feature selection for the CIC-2017 dataset with the SVM algorithm on the CSDPayload+N-Gram feature with a 4-Gram accuracy rate of 99.86%, MIB- Dataset 2016 with the 2016 algorithm. SVM and CSPayload feature +N-Gram with 100% accuracy for 4-Gram, H2N-Payload Dataset with SVM Algorithm, and CSDPayload+N-Gram feature with 100% accuracy for 4-Gram. As a comparison, the KNN algorithm for 4-Gram has an accuracy rate of 99.44%, and the Neural Network Algorithm has an accuracy rate of 100% for 4-Gram. Thus, the best algorithm for DDoS detection is SVM with Hybrid N-Gram (4-Gram).
Co-Authors Abdul Hamid Abdul Razaq Fatta Utama Adeh Berydika Andi Maslan Andi Maslan Anggiat Marubah Siringo ari purnama, ari purnama ARSYAD SUMANTIKA Arwin, elfani Azri Irawan Brilliant Hillary Sandy Daeli, Fonirius Dwi Saputra, Kevin Ganda Sirait Gregorius Teheq Buyanaya, Gregorius Teheq Buyanaya Halimah Tussa'Adiyah, Halimah Handoko, Koko Hotma Pangaribuan Hotma Pangaribuan Hotma Pangaribuan IRWANSYAH Junellson Kamaruddin Malik Bin Mohamad Lase, Fanotona Lubis, Kahfi Rama Putra Manik, Yosep Pangihutan Martan, Martan Gelisa Halawa Marubah Siringo, Anggiat Mendrofa, Nober Six Salvanius Muhammad Nur Sapi'i Mukhsinin, Nuriel Niati, Endang Pangaribuan, Hotma Putri, Sartika Diana rajagukguk, dormintang rajagukguk Renaldi, Encik Yoega rikson pandapotan tampubolon RIZKIANA, WIKI INDAH RIZKIANA Sabbram Agus Setiawan sam, samuel julianda berkat waruwu Sandy Sandy Saputra, Rios sari, anggi wijaya Setia Susilawati Setiawan, Sabbram Agus Silalahi, Putri Rosayanti Simanjuntak, Pastima Sirait, David Arif Setiawan Sirait, Ganda Situmorang, Nopriadi Stevent Suharyanto, Cosmas Eko supianto supianto Supianto Supianto, Supianto Very Karnadi Very Karnadi Virginiawan, Virginiawan Zendate, Sabar Serius