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All Journal Jurnal Pseudocode Jurnal Computech & Bisnis (e-Journal) Acman: Accounting and Management Journal Informatics Management, Engineering and Information System Journal Electronic, Business, Management and Technology Journal Jurnal Accounting Information System (AIMS) INTERNAL (Information System Journal) Majalah Bisnis & IPTEK Journal of Economics, Management, and Entrepreneurship Jurnal Pengabdian Masyarakat: Bisnis dan Iptek (JPMBISTEK)
Asep Ririh Riswaya
STMIK Mardira Indonesia
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Environmental Science Industrial & Manufacturing Engineering Library & Information Science Social Sciences Other

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Journal : Jurnal Pseudocode

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Penerapan Algoritma Random Forest Dan Support Vector Machine Untuk Deteksi Distributed Denial of Service (DDoS) Riswaya, Asep Ririh; Fadlil, Abdul; Yudhana, Anton
Jurnal Pseudocode Vol 13 No 1 (2026): Volume 13 Nomor 1 Februari 2026
Publisher : UNIB Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33369/pseudocode.13.1.28-35

Abstract

Distributed Denial of Service (DDoS) attacks significantly threaten the availability of modern network services by overwhelming resources with malicious traffic. The increasing complexity of these attacks, including multi-vector and low-rate variants, reduces the effectiveness of traditional detection methods based on signatures and static rules. This study explores the effectiveness of Random Forest (RF) and Support Vector Machine (SVM) algorithms in detecting DDoS attacks using the CICDDoS2019 dataset, focusing on the impact of various decision threshold values on performance. The CICDDoS2019 dataset consists of 431,371 network traffic flows with 80 numerical features. Preprocessing involves eliminating null values, standardizing numerical attributes, and encoding labels into binary classifications of normal and DDoS traffic. The dataset is then divided into training and testing sets at a 70:30 ratio. Performance evaluation is done using a confusion matrix to calculate accuracy, precision, recall, and F1-score. Results show that both algorithms perform well, but Random Forest offers greater consistency, with a threshold of 0.5 achieving the best balance in metrics.
Co-Authors . Komarudin Abdul Fadlil Abdurahman Fauzi Achyar Nuraulia Rahman Ade Supriatna Aep Syaripudin Agni Ahmad Saripudin Agung Wahana Agung Wahana Agung Wahana Alfi Yudin Raihan Anisa, Tasya Nurul Anjas Tryana Anton Yudhana Aris Koharudin Farras Asep Maulana Ashwin Sasongko Budiman, Dheni Apriantsani Dadi Rosadi Damayanti, Elok Dimas Nurdiansyah Eka P. Sakti Fadhlan Hadyan Nadhir Fika Amalia, Fika Fikri Irawan Abdurahman Hadian Hadian Hadian Hadian Hasan Abdurahman Hasyifa Nur Fadhillah Heri Wahyudi Jajat sudrajat Jajat Sudrajat Kiki Agustin Marjito Marjito Maulana, Aris Ilham Muhammad Naufal Nengsih, Nengsih Noerrohmah, Syifa Oji Tiongkos Maruli Chrismas Oktavia Oktavia Oktavia Oktavia Retnowaty, Agung Tri Ricky Rohmanto Rizky Panji Santoni Rizky Panji Santoni Rizqiati, Fitri Sandri Arya Fikriawan Septian Adiraharja Sigit Aprilian Sirozudin Ibnu Farhan Solihin Solihin Suprianto Suprianto Suwandana Suryana Putera Tay, En Yusup Saepulloh