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All Journal JUTI: Jurnal Ilmiah Teknologi Informasi Journal of Information Systems Engineering and Business Intelligence Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JSAI (Journal Scientific and Applied Informatics) Walisongo Journal of Information Technology Brilliance: Research of Artificial Intelligence Abdimas Indonesian Journal Jurnal Pengabdian Masyarakat
Narandha Arya Ranggianto
Universitas Jember
Religion Computer Science & IT Decision Sciences, Operations Research & Management Dentistry Economics, Econometrics & Finance Education Engineering Environmental Science Industrial & Manufacturing Engineering Library & Information Science Mathematics Social Sciences Other

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Journal : JSAI (Journal Scientific and Applied Informatics)

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Perbandingan Performa Algoritma Random Tree, K-NN, dan A-NN untuk Deteksi Serangan DDoS pada Software Defined Network (SDN) Akbar Pandu Segara; Muhammad Andryan Wahyu Saputra; Narandha Arya Ranggianto
JSAI (Journal Scientific and Applied Informatics) Vol 8 No 2 (2025): Juni
Publisher : Fakultas Teknik Universitas Muhammadiyah Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36085/jsai.v8i2.8387

Abstract

Software-Defined Networks (SDNs) with a centralized architecture are vulnerable to Distributed Denial of Service (DDoS) attacks, which can cause widespread network service failures. This study aims to compare the performance of three Machine Learning algorithms—K-Nearest Neighbor (K-NN), Artificial Neural Network (ANN), and Random Tree—in detecting DDoS attacks in an SDN environment. The DDoS-SDN dataset, consisting of 104,345 rows and 23 columns, was used with a data split of 70% for training and 30% for testing. Evaluation was conducted using accuracy, precision, recall, F1-score, and AUC-ROC metrics. The results showed that ANN achieved the best performance with an accuracy of 96.85%, precision of 94.35%, recall of 97.79%, F1-score of 96.04%, and AUC of 0.994, followed by K-NN with an accuracy of 88.89% and Random Tree with the lowest accuracy of 86.49%. The superiority of ANN is attributed to its ability to capture complex non-linear patterns, perform automatic feature extraction, and adapt to the heterogeneity of data from the 22 features used. These findings indicate that ANN is the optimal choice for implementing a real-time DDoS attack detection system in an SDN environment, providing a strong foundation for the development of intelligent and adaptive Machine Learning-based network security systems
Co-Authors Afandi, Khoirunnisa Akbar Pandu Segara Baihaqi, Yustri Budi Yuswanto, Istatuk Chastine Fatichah Damar Novtahaning Diana Purwitasari Dwi Wijonarko Isti Fadah M. Habibullah Arief Martiana Kholila Fadhil Muhammad Andryan Wahyu Saputra Rizka Wakhidatus Sholikah, Rizka Wakhidatus RR. Ella Evrita Hestiandari Sherly Rosa Anggraeni Vika Octaviani Yudha Alif Auliya