Classification of Distributed Denial Service (DDoS) Attacks Using the K-Nearest Neighbor (KNN) Method

R.Adinda Hamba Banju; Fajar Hariadi; Raynesta Mikaela Indri Malo

doi:10.59934/jaiea.v5i1.1341

Authors

R.Adinda Hamba Banju Universitas Kristen Wira Wacana Sumba
Fajar Hariadi Universitas Kristen Wira Wacana Sumba
Raynesta Mikaela Indri Malo Universitas Kristen Wira Wacana Sumba

DOI:

https://doi.org/10.59934/jaiea.v5i1.1341

Keywords:

Network Security, Distributed Denial of Service (DDoS), K-Nearest Neighbor (KNN), Attack Classification

Abstract

Distributed Denial of Service (DDoS) attacks pose a significant security threat by disrupting network services through a flood of data traffic or exploiting system vulnerabilities. Early detection of DDoS attacks is essential to reduce their potential impact. This study aims to classify DDoS attacks using the K-Nearest Neighbor (KNN) algorithm to improve network security. The research data is sourced from a publicly available DDoS Software-Defined Networking (SDN) dataset. The research stages include data collection, pre-processing, implementation of the KNN algorithm, and model evaluation. Data pre-processing involves data cleansing, feature transformation, and normalization to optimize model performance. The KNN algorithm determines the number of Ks of the nearest neighbor and uses geometric distances to classify DDoS attacks. The conclusion of this study assesses the accuracy of the KNN model in detecting DDoS attacks. The results of the evaluation showed that the KNN model reached a level of accuracy

Downloads

Download data is not yet available.

References

Y. Ariyanto, V. A. H. Firdaus, and H. Pramana, "Classification of DOS and Probing Attack Types on IDS Using the K- Nearest Neighbor Method," Sem. Inform. Apps. Polynesian, vol. 3, no. ISSN 2460-1160, pp. 1–5, 2020.

M. Alfi, "Cyber Security Risk Analysis in the Digital Transformation of Public Services in Indonesia," J. Study. Strat. Nas Resilience., vol. 6, no. 2, 2023, doi: 10.7454/jkskn.v6i2.10082.

L. Sari, M. N. Faiz, and A. W. Muhammad, "Comparison of Machine Learning Approaches in Detection of DDoS Attacks on Computer Networks," Infotek, vol. 16, no. 1, pp. 153–159, 2025, doi: 10.35970/infotekmesin.v16i1.2556.

M. Fluoride Fibrianda and A. Bhawiyuga, "Comparative Analysis of the Accuracy of Attack Detection in Computer Networks Using Naïve Bayes Method and Support Vector Machine (SVM)," J. Pengemb. Technology. Inf. and Computing Science., vol. 2, no. 9, pp. 3112–3123, 2018, [Online]. Available: http://j-ptiik.ub.ac.id

M. Iqbal, R. Rohmat Saedudin, and M. Fathinuddin, "Comparative Analysis of K-Nearest Neighbor and Naïve Bayes Accuracy for Classification of Computer Network Attack Data," EDUSAINTEK J. Education, Science and Technology., vol. 9, no. 3, pp. 920–929, 2022, doi: 10.47668/edusaintek.v9i3.611.

M. F. E. Erlangga, N. Fahriani, and ..., "Detection of Syn Flood Attacks on Servers Using the K-Nearest Neightbor Algorithm Method," Sem. Nas. Technology. Inf. Computer Science., vol. 2, no. 1, pp. 68–72, 2023, [Online]. Available: https://journal.unilak.ac.id/index.php/Semaster/article/view/18458

D. Surya Prasetyo, K. Auliasari, and M. Ridho Putra Syalabi, "Classification of Network Attacks Using the K-Nearest Neighbour Method on Network History Data," Pros. SANDIX, vol. 7, no. 1, pp. 63–71, 2023, doi: 10.36040/seniati.v7i1.7874.

A. Bustami and S. Bahri, "Threats, Attacks and Protection Measures on Network or Information System Security: Systematic Review," Unistek, vol. 7, no. 2, pp. 59–70, 2020, doi: 10.33592/unistek.v7i2.645.

Z. I. Sumayyah, S. D. S. Permana, M. Tsabit, and A. Setiawan, "Application and Mitigation of Slowloris Technique in Distributed Denial-of-Service (DDos) Attacks on Illegal Websites with Kali Linux," J. Internet Softw. Eng., vol. 1, no. 2, p. 14, 2024, doi: 10.47134/pjise.v1i2.2694.

J. Supriyanto, D. Alita, and A. R. Isnain, "Application of K-Nearest Neighbor (K-NN) Algorithm for Public Sentiment Analysis of Online Learning," J. Inform. and Software Engineering, vol. 4, no. 1, pp. 74–80, 2023, doi: 10.33365/jatika.v4i1.2468.