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Danang Danang
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Journal : International Journal of Information Engineering and Science

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Hybrid CNN GRU Framework for Early Detection and Adaptive Mitigation of DDoS Attacks in SDN using Image Based Traffic Analysis Danang Danang; Indra Ava Dianta; Agustinus Budi Santoso; Siti Kholifah
International Journal of Information Engineering and Science Vol. 2 No. 3 (2025): August : International Journal of Information Engineering and Science
Publisher : Asosiasi Riset Teknik Elektro dan Infomatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/ijies.v2i2.292

Abstract

The threat of Distributed Denial of Service (DDoS) is increasing develop along with increasing use of the Internet of Things (IoT) and Software-Defined Networking (SDN) architecture . Although SDN provides convenience in management network , properties its centralized control make it prone to to flooding attacks that can paralyze controller performance . Detection method conventional , such as approach statistics and machine learning, still own limitations in matter accuracy , high false positive rate , and dependence on extracted features manually . To overcome problem said , research This propose a hybrid deep learning based DDoS detection and mitigation model that combines Convolutional Neural Network (CNN) to extraction feature spatial from RGB and Gated Recurrent Unit (GRU) images for understand temporal correlation between traffic data network . System tested through network test-bed Mininet based with Ryu/Floodlight controller, using simulation DDoS attacks (Hping3, LOIC) and normal traffic (video streaming, HTTP server). Traffic data cross recorded in PCAP format, processed become RGB image measuring 200×200 pixels, and labeled based on type traffic . Evaluation results with metric accuracy , precision, recall, F1-score, and MCC show that the CNN–GRU model has performance more superior compared to baseline approaches such as CNN-only, GRU-only, as well as classical ML methods such as SVM and Random Forest. In addition , the system capable apply mitigation adaptive through automatic flow rule creation on edge switches. Findings This confirm that effective deep learning- based spatial -temporal hybrid approach in increase detection early and response DDoS attacks on SDN networks adaptive and real-time.  
Co-Authors Agus Priyadi Agustinus Budi Santoso Ahmad Ashifuddin Aqham Ahmad Zainudin Aldo Raditya Pangestu Andik Prakasa Hadi Arie Atwa Magriyanti Budi Santoso Dani Sasmoko Daniel Rudjiono Dendy Kurniawan Dewi Retno Putri Eka Satria Wibawa Ekky Fredyan Eko Siswanto Eko Siswanto Eni Muhadi Fa`iq Khotibul Umam Febri Adi Prasetya Febryantahanuji Febryantahanuji Greget Widhiati Hanif Assabib Rosyid Hendri Rasminto Iman Saufik Suasana Indra Ava Dianta Joseph Teguh Santoso Kenny Setiawan Khoirur Rozikin Kusumajaya, Robby Andika Laily Eka Andrianni Lilis Suryani Mat Toyib Maya Utami Dewi Muhamad Khoirul Umam Muhammad Ainun Najib Muhammad Fauzi Alfikri Mukhammad Ibnu Khalim NUR FAUZIAH Nuris Dwi Setiawan PADJAR SETYO BUDI Priyadi Priyadi Priyo Wibowo Puteri Anindya Maulan Raden Mohamad Herdian Bhakti Rashad Huseynaga Asgarov Rizky Ni'ma Febriani Siswanto Siswanto Siswanto Siswanto Siswanto Siswanto Siswanto, Edy Siti Kholifah Sulartopo Sulartopo Suprapti Suprapti Suwardi Suwardi Tanveer Shah Teguh Setiadi Toni Wijanarko Adi Putra Uswatul Chasanah Widya Aryani Wijaya Heti Susanti Zaenal Mustofa Zellius Ragiliawan