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All Journal International Journal of Informatics and Communication Technology (IJ-ICT) Jurnal Simetris Jurnal Penelitian Pendidikan Scientific Journal of Informatics Seminar Nasional Informatika (SEMNASIF) SISFOTENIKA JIKO (Jurnal Informatika dan Komputer) MATAPPA: Jurnal Pengabdian Kepada Masyarakat JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Building of Informatics, Technology and Science Jurnal Manajemen Informatika dan Sistem Informasi JOURNAL OF INFORMATION SYSTEM MANAGEMENT (JOISM) Abdimasku : Jurnal Pengabdian Masyarakat Infotech: Jurnal Informatika & Teknologi Yayasan Cita Cendikiawan Al Khwarizmi Jurnal Pengabdian Masyarakat - Teknologi Digital Indonesia Journal of Intelligent Software Systems Jurnal Teknologi Informasi Jurnal INFOTEL Scientific Journal of Informatics
Rikie Kartadie
Universitas Teknologi Digital Indonesia
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Journal : Scientific Journal of Informatics

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Optimizing LSTM-CNN for Lightweight and Accurate DDoS Detection in SDN Environments Kartadie, Rikie; Kusjani, Adi; Kusnanto, Yudhi; Harnaningrum, Lucia Nugraheni
Scientific Journal of Informatics Vol. 12 No. 2: May 2025
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v12i2.24531

Abstract

Purpose: This study optimizes the LSTM-CNN model to detect Distributed Denial of Service (DDoS) attacks in Software-Defined Networking (SDN)-based networks and improves accuracy, computational efficiency, and class imbalance handling. Methods: We developed an Improved LSTM-CNN by removing the Conv1D layer, reducing LSTM units to 64, and using 21 features with a 5-timestep approach. The InSDN dataset (50,000 samples) was preprocessed with one-hot encoding, MinMaxScaler normalization, and sequence formation. Class imbalance was managed using class weights (0:2.0, 1:0.5) instead of SMOTE, with performance compared against Baseline LSTM-CNN and Dense-only models optimized with the Sine Cosine Algorithm (SCA). Result: The Improved LSTM-CNN achieved 0.99 accuracy, 0.93 F1-score for Benign traffic, and 1.00 for Malicious traffic, with ~25,000 parameters and 125 ms inference time on Google Colab. It outperformed Baseline LSTM-CNN (0.08 accuracy) and was more efficient than Dense-only (46,000 parameters), with a false positive rate of ~1%. Novelty: This research presents a lightweight, efficient DDoS detection solution for SDN, leveraging temporal modeling and class weights, suitable for resource-constrained controllers like OpenDaylight or ONOS. However, its generalization is limited by dataset diversity, necessitating broader validation.
Co-Authors Aditomo, Sigit Aloysius Agus Subagyo Andriyani, Widyastuti Anggoro, Pius Dian Widi Asyahri Hadi Nasyuha Atni Hadi, Teguh Hayatul Auliadi, Anissa Sahda Bagas Triaji Bagus Fahrudin Jamal Bambang Purnomosidi Dwi Putranto da silva, Ivònia Fàtima Ruas Dara Kusumawati Dewa, Eddy Putra Eddy Putra Dewa Edi Kurniawan Edy Prayitno Elyas, Ananda Hadi Erna Hudianti Femi Dwi Astuti FIRMANDA, EKA ADI Handayani, Latifah Nurrohmah Hanifa, Sizka L. Hendra Maryanto Hikmanto, Riki Israel Mnsen, Heman Koreri Kabahing, Sepfanner kholik, Moh. Abdul KURNAEDI, DIDI Leswanto, Tomi Lucia Nugraheni Harnaningrum Mardika, Ardeana Galih Muhammad Guntara Muhammad Kurnia Ramadhan Nanis Hairunisya Nenen Isnaeni Ngubaidillah, Ahmad Ngubaidillah, Ahmad Nugroho, Ari Fauzi Mukti Nugroho, Muhammad Agung Nur aini, Nur aini Panggayuh, Vertika Prayitna, Adiyuda Purnomosidi, Bambang Ragil Murdiantoro Aji Rizky, Firahmi Robby Cokro Buwono Robertus Saptoto Robertus Saptoto Rozi, Fahrur Sizka L. Hanifa Subiyantoro, Hari Syabani, Dwi Nur Tommy Suryanto Totok Suprawoto Vertika Panggayuh Widyastuti Andriyani Winarno, Basuki Heri Xavier, Francisco Y Yohakim Mawarta Yudhi Kusnanto Zulham Zulham