Blockchain and SVM Integration for Distributed DDoS Attack Detection

Authors

  • Septua Ginta Putra Hia Informatika, Fakultas Teknologi Komunikasi dan Informatika, Universitas Nasional
  • Nur Hayati Informatika, Fakultas Teknologi Komunikasi dan Informatika, Universitas Nasional
  • Djarot Hindarto Informatika, Fakultas Teknologi Komunikasi dan Informatika, Universitas Nasional
  • Asrul Sani Magister Teknologi Informasi, Fakultas Teknologi Komunikasi dan Informatika, Universitas Nasional

DOI:

10.33395/sinkron.v10i1.15483

Keywords:

Support Vector Machine, Blockchain, DDoS Detection, Cybersecurity, Machine Learning

Abstract

Rapid developments in information technology have increased dependence on network services, but have also triggered an increase in cyber threats such as Distributed Denial of Service (DDoS). These attacks can paralyze systems by flooding servers with simultaneous fake traffic. Conventional rule-based detection methods are now less effective in dealing with dynamic attack patterns, requiring an adaptive approach based on machine learning. This research develops a Support Vector Machine (SVM) model enhanced with Blockchain technology to improve accuracy and data security in detecting DDoS attacks. The dataset used is CICDDoS2023 from the Canadian Institute for Cybersecurity, which contains various variants of modern DDoS attacks. The research stages include data pre-processing, training the SVM model using the RBF kernel, and integrating Blockchain with training data hash recording through a smart contract using Remix Ethereum to ensure data integrity. Performance evaluation was carried out using accuracy, precision, recall, and F1-score metrics based on the confusion matrix results. The integration of SVM and Blockchain showed an increase in security and detection accuracy compared to conventional SVM models. This approach not only improves the reliability of the DDoS attack detection system, but also creates a transparent and tamper-proof data validation mechanism. The research results are expected to contribute to the development of adaptive, decentralized network security systems with a high level of confidence in attack detection results.

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References

Agrawal, R., Pratap Srivastava, A., & Sugiyama, A. (n.d.). International Conference on Sustainability in Digital Transformation Era: Driving Innovative & Growth.

Aurora, C., Henry, H., Handra, T., Sutisna, F., & Parker, J. (2025). Implementing Blockchain Technology to Strengthen Privacy and Authenticity in University Records. Jurnal MENTARI: Manajemen, Pendidikan Dan Teknologi Informasi, 4(1), 83–93. https://doi.org/10.33050/mentari.v4i1.912

Bhayo, J., Shah, S. A., Hameed, S., Ahmed, A., Nasir, J., & Draheim, D. (2023). Towards a machine learning-based framework for DDOS attack detection in software-defined IoT (SD-IoT) networks. Engineering Applications of Artificial Intelligence, 123. https://doi.org/10.1016/j.engappai.2023.106432

Doriguzzi-Corin, R., Millar, S., Scott-Hayward, S., Martinez-Del-Rincon, J., & Siracusa, D. (2020). Lucid: A Practical, Lightweight Deep Learning Solution for DDoS Attack Detection. IEEE Transactions on Network and Service Management, 17(2), 876–889. https://doi.org/10.1109/TNSM.2020.2971776

Fadel, A., & Ali, A. (n.d.). International Journal of Formal Sciences: Current and Future Research Trends (IJFSCFRT) DDOS (Distributed Denial of Service) Attack Detection and Mitigation Using Statistical and Machine Learning Methods in SDN (Software-Defined Networking). https://ijfscfrtjournal.isrra.org/index.php/Formal_Sciences_Journal/index

Fathima, A., Devi, G. S., & Faizaanuddin, M. (2023). Improving distributed denial of service attack detection using supervised machine learning. Measurement: Sensors, 30. https://doi.org/10.1016/j.measen.2023.100911

Giryes, R., Shafir, L., & Wool, A. (2024). A Flow is a Stream of Packets: A Stream-Structured Data Approach for DDoS Detection. http://arxiv.org/abs/2405.07232

Huang, J., Zhou, K., Xiong, A., & Li, D. (2022). Smart Contract Vulnerability Detection Model Based on Multi‐Task Learning. Sensors, 22(5). https://doi.org/10.3390/s22051829

Islam, S., & Rahman, M. S. (n.d.). LogStamping: A blockchain-based log auditing approach for large-scale systems.

Kezadri Hamiaz, M., & Driss, M. (2025). Ethereum Smart Contracts Under Scrutiny: A Survey of Security Verification Tools, Techniques, and Challenges. In Computers (Vol. 14, Issue 6). Multidisciplinary Digital Publishing Institute (MDPI). https://doi.org/10.3390/computers14060226

Kiani, R., & Sheng, V. S. (2024). Ethereum Smart Contract Vulnerability Detection and Machine Learning-Driven Solutions: A Systematic Literature Review. In Electronics (Switzerland) (Vol. 13, Issue 12). Multidisciplinary Digital Publishing Institute (MDPI). https://doi.org/10.3390/electronics13122295

Kurisaka, H., Su, Y., Nguyen, P. Le, Nguyen, K., & Sekiya, H. (2025). Performance evaluation of ethereum consensus mechanisms in IoT-blockchain systems using resource-constrained devices. Cluster Computing, 28(12). https://doi.org/10.1007/s10586-025-05503-w

Liu, Z., Wang, Y., Feng, F., Liu, Y., Li, Z., & Shan, Y. (2023). A DDoS Detection Method Based on Feature Engineering and Machine Learning in Software-Defined Networks. Sensors, 23(13). https://doi.org/10.3390/s23136176

Ortet Lopes, I., Zou, D., Ruambo, F. A., Akbar, S., & Yuan, B. (2021). Towards Effective Detection of Recent DDoS Attacks: A Deep Learning Approach. Security and Communication Networks, 2021. https://doi.org/10.1155/2021/5710028

https://www.kaggle.com/datasets/mastole/ddos-ciciot2023

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How to Cite

Hia, S. G. P. ., Hayati, N. ., Hindarto, D. ., & Sani, A. . (2026). Blockchain and SVM Integration for Distributed DDoS Attack Detection. Sinkron : Jurnal Dan Penelitian Teknik Informatika, 10(1), 75-84. https://doi.org/10.33395/sinkron.v10i1.15483

Issue

Vol. 10 No. 1 (2026): Article Research January 2026

Section

Articles

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Copyright (c) 2025 Septua Ginta Putra Hia, Nur Hayati, Djarot Hindarto, Asrul Sani

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

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