Simulasi dan Analisis Strategi Hybrid Teaming Menggunakan Algoritma Naive Bayes dalam Deteksi Serangan Distributed Denial of Service (DDoS)
-
Aprilian Gevindo
*
Universitas Putra Indonesia YPTK Padang, Padang,
Indonesia
- Yuhandri Yuhandri Universitas Putra Indonesia YPTK Padang, Padang, Indonesia
- Billy Hendrik Universitas Putra Indonesia YPTK Padang, Padang, Indonesia (*) Corresponding Author
Abstract
Cyber attacks, particularly Distributed Denial of Service (DDoS), have become a serious threat to the availability of servers and other network infrastructure. These attacks can paralyze services on large-scale networks by flooding the target system with extremely high traffic. Based on this, the objective of this research is to simulate and analyze a Hybrid Teaming strategy using the Naïve Bayes algorithm. This strategy simulates structured collaboration between the Red Team (attackers), Blue Team (defenders), and Purple Team (evaluators) to test resilience while comprehensively strengthening the security posture. The Naïve Bayes algorithm is one of the best algorithms in Machine Learning and excels at performing data classification processes. The performance of the Naïve Bayes algorithm combined with the Hybrid Teaming strategy is developed into an intelligent detection system. This system is trained using 10,000 data points from a public dataset and 1,688 data points from the network logs of the Tapan Regional General Hospital (RSUD). Based on the data analysis results, the model training outcomes fall into the perfect category, with accuracy, precision, recall, and F1-score achieving a result of 100%. The model was then implemented on a server and a MikroTik router within a simulation environment that replicates the Tapan RSUD network. The test results on these two components show that the system successfully detected various Flooding attack patterns with a detection accuracy of 100%. The system is capable of responding automatically by blocking the attacker's IP (Internet Protocol) address at both layers, as well as sending real-time notifications via WhatsApp and Email. The contribution of this research results in a comprehensive and effective cybersecurity defense framework.
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References
I. Rahmadaniar, D. A. A. Tondang, B. S. Fernando, and A. Setiawan, “Implementasi Firewall Menggunakan Iptables untuk Melindungi Server dari Serangan DDoS,” Journal of Internet and Software Engineering, vol. 1, no. 3, p. 10, Jun. 2024, doi: 10.47134/pjise.v1i3.2564.
Y. I. Mahendra and R. E. Putra, “Penerapan Algoritma Gradient Boosted Decision Tree (GBDT) untuk Klasifikasi Serangan DDoS,” Journal of Informatics and Computer Science, vol. 6, no. 1, 2024, doi: 10.26740/jinacs.v6n01.p158-166.
A. R. Syujak, K. Diantoro, A. Soderi, and P. A. Sucipto, “Integrasi deep packet inspection dengan intrusion detection system (ids) untuk identifikasi serangan ddos dalam jaringan skala besar,” Jurnal Minfo Polgan, vol. 13, no. 2, pp. 1971–1975, 2024, doi: 10.33395/jmp.v13i2.14324.
K. Ruswandi, M. R. Z. Pohan, K. V. Halim, and S. N. Neyman, “Strategi Pencegahan Efektif terhadap Serangan DDoS Slowloris menggunakan Kali Linux dan Linux Mint,” Journal of Technology and System Information, vol. 1, no. 4, p. 11, 2024, doi: 10.47134/jtsi.v1i4.2645.
S. M. S. Munawarah and E. A. Winanto, “Deteksi Serangan DDoS SYN Flood Pada Jaringan Internet of Things (IoT) Menggunakan Metode Deep Neural Network (DNN),” Jurnal Informatika Dan Rekayasa Komputer (JAKAKOM), vol. 4, no. 1, pp. 982–991, 2024, doi: 10.33998/jakakom.2024.4.1.1710.
A. Steshenko, “Automating Correlation Between Attacks and Detection in Purple Team Exercises,” 2023. [Online]. Available: http://www.usn.no
L. G. Hedley, M. S. Bennett, J. Love, J. Houpt, S. Brown, and A. Eidels, “The relationship between teaming behaviours and joint capacity of hybrid human-machine teams,” in Proceedings of the annual meeting of the cognitive science society, 2023.
I. H. Sarker, H. Janicke, N. Mohammad, P. Watters, and S. Nepal, “AI Potentiality and Awareness: A Position Paper from the Perspective of Human-AI Teaming in Cybersecurity,” Sep. 2023, [Online]. Available: http://arxiv.org/abs/2310.12162
C. Wang et al., “Leveraging Reinforcement Learning in Red Teaming for Advanced Ransomware Attack Simulations,” Jun. 2024, [Online]. Available: http://arxiv.org/abs/2406.17576
B. Kotwani, M. R. Sawant, and D. S. Chopra, “Red Teaming vs. Blue Teaming: A Comparative Analysis of CyberSecurity Strategies in the Digital Battlefield,” INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT, vol. 07, no. 12, pp. 1–11, Dec. 2023, doi: 10.55041/ijsrem27675.
A. Beutel, K. Xiao, J. Heidecke, and L. Weng, “Diverse and Effective Red Teaming with Auto-generated Rewards and Multi-step Reinforcement Learning,” arXiv preprint arXiv:2412.18693, 2024.
A. Z. Fazilat et al., “AI-based cleft lip and palate surgical information is preferred by both plastic surgeons and patients in a blind comparison,” The Cleft Palate Craniofacial Journal, vol. 62, no. 9, pp. 1542–1548, 2025, doi: 10.1177/10556656241266368.
M. Al-Azzawi, D. Doan, T. Sipola, J. Hautamäki, and T. Kokkonen, “Red Teaming with Artificial Intelligence-Driven Cyberattacks: A Scoping Review,” arXiv preprint arXiv:2503.19626, 2025.
D. Novianto, Y. S. Japriadi, L. Tommy, and S. Sujono, “Implementasi Virtual Private Network Menggunakan Sstp Untuk Keamanan Akses Ke Network Attached Storage Server,” JUSIM (Jurnal Sistem Informasi Musirawas), vol. 10, no. 1, pp. 1–13, 2025.
F. Bianchi, E. Bassetti, and A. Spognardi, “Scalable and automated Evaluation of Blue Team cyber posture in Cyber Ranges,” in Proceedings of the 39th ACM/SIGAPP Symposium on Applied Computing, 2024, pp. 1539–1541.
S. Wang, Y. Li, and F. Chen, “Optimizing blue team strategies with reinforcement learning for enhanced ransomware defense simulations,” Authorea Preprints, 2024.
S. Wang, Y. Li, and F. Chen, “Optimizing blue team strategies with reinforcement learning for enhanced ransomware defense simulations,” Authorea Preprints, 2024.
F. Bianchi, E. Bassetti, and A. Spognardi, “Scalable and automated Evaluation of Blue Team cyber posture in Cyber Ranges,” in Proceedings of the 39th ACM/SIGAPP Symposium on Applied Computing, 2024, pp. 1539–1541.
A. J. Titus and A. H. Russell, “The Promise and Peril of Artificial Intelligence--Violet Teaming Offers a Balanced Path Forward,” arXiv preprint arXiv:2308.14253, 2023.
Y. Ge and Q. Zhu, “Mega-pt: A meta-game framework for agile penetration testing,” in International Conference on Decision and Game Theory for Security, Springer, 2024, pp. 24–44, doi: 10.1007/978-3-031-74835-6_2.
L. Svotwa, A. Ganga, and T. Kachote, Security Engineering with AI for Purple Teaming in Southern Africa. 2025. doi: 10.13140/RG.2.2.16382.29762.
J. K. Muchina, J. Njihia, and A. Wausi, “The Role of Shared Information Systems Knowledge, Information Systems Resources, and Information System Function Performance,” Electronic Journal of Knowledge Management, vol. 23, no. 2, pp. 149–165, 2025, doi: 10.34190/ejkm.23.2.3740.
F. Putra, “Penerapan Teknologi Machine Learning dalam Deteksi Dini Penyakit Pada Tanaman Pangan,” Jurnal Kolaborasi Sains dan Ilmu Terapan, vol. 3, no. 1, pp. 1–5, 2024, doi: 10.69688/juksit.v3i1.50.
R. V. Rahadyan and R. A. Widyanto, “Literatur Riview: Implementasi Machine Learning pada E Learning System,” in Prosiding University Research Colloquium, 2023, pp. 723–729.
A. S. Sitio and F. A. Sianturi, “Penerapan Algoritma Machine Learning dalam Analisis Pola Perilaku Penggunaan Internet,” Dike, vol. 2, no. 2, pp. 46–51, 2024, doi: 10.69688/dike.v2i2.102.
P. Triya, N. Suarna, and N. D. Nuris, “Penerapan Machine Learning Dalam Melakukan Prediksi Harga Saham Pt. Bank Mandiri (Persero) Tbk Dengan Algoritma Linear Regression,” JATI (Jurnal Mahasiswa Teknik Informatika), vol. 8, no. 1, pp. 1207–1214, 2024, doi: 10.36040/jati.v8i1.8958.
B. Ramadhani, R. R. Suryono, and K. Kunci, “Komparasi Algoritma Naïve Bayes dan Logistic Regression Untuk Analisis Sentimen Metaverse,” vol, vol. 8, pp. 714–725, 2024.
K. B. P. Y. Perkasa and F. E. Purwiantono, “Sistem Rekomendasi Jurusan Menggunakan Algoritma Naïve Bayes Gaussian Berbasis Web,” J-INTECH (Journal of Information and Technology), vol. 11, no. 2, pp. 361–370, 2023.
P. G. Aryanti and I. Santoso, “Analisis Sentimen Pada Twitter Terhadap Mobil Listrik Menggunakan Algoritma Naive Bayes,” IKRA-ITH Informatika: Jurnal Komputer Dan Informatika, vol. 7, no. 2, pp. 133–137, 2023.
W. A. Firmansyach, U. Hayati, and Y. A. Wijaya, “Analisa Terjadinya Overfitting Dan Underfitting Pada Algoritma Naive Bayes Dan Decision Tree Dengan Teknik Cross Validation,” JATI (Jurnal Mahasiswa Teknik Informatika), vol. 7, no. 1, pp. 262–269, 2023, doi: 10.36040/jati.v7i1.6329.
J. Castro, “Using Red and Purple Teaming to Reduce Cyber Risk,” Aug. 2024.
M. Feffer, A. Sinha, W. H. Deng, Z. C. Lipton, and H. Heidari, “Red-Teaming for generative AI: Silver bullet or security theater?,” in Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, 2024, pp. 421–437.
E. Karhunen, “Purple teaming in system hacking,” Computer science, 2025.
N. Khoirunnisaa, K. N. N. Kesuma, S. Setiawan, and A. Y. P. Yusuf, “Klasifikasi Teks Ulasan Aplikasi Netflix Pada Google Play Store Menggunakan Algoritma Naive Bayes dan SVM,” SKANIKA: Sistem Komputer dan Teknik Informatika, vol. 7, no. 1, pp. 64–73, 2024, doi: 10.36080/skanika.v7i1.3138.
G. Honestya, S. Defit, and G. W. Nurcahyo, “Penerapan Naive Bayes untuk Memilih Produk Berdasarkan Jenis Kulit di Toko Kosmetik,” Jurnal KomtekInfo, pp. 274–280, 2024.
Y. Naufal, R. Putro, A. Afriansyah, and R. Bagaskara, “Penggunaan Algoritma Gaussian Naïve Bayes & Decision Tree Untuk Klasifikasi Tingkat Kemenangan Pada Game Mobile Legends,” JUKI : Jurnal Komputer dan Informatika, vol. 6, 2024.
Fenilinas Adi Artanto, “Analisis Sentimen Opini Publik terhadap Fenomena Bunuh Diri Mahasiswa di Twitter Menggunakan Algoritma Naive Bayes,” SATESI: Jurnal Sains Teknologi dan Sistem Informasi, vol. 4, no. 1, pp. 70–77, Apr. 2024, doi: 10.54259/satesi.v4i1.2908.
E. N. Cahyo, E. Susanti, and R. Y. Ariyana, “Model Machine Learning Untuk Klasifikasi Kesegaran Daging Menggunakan Arsitektur Transfer Learning Xception,” JUSTIN (Jurnal Sistem dan Teknologi Informasi), vol. 11, no. 2, pp. 371–376, 2023, doi: 10.26418/justin.v11i2.57517.
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Copyright (c) 2026 Aprilian Gevindo, Yuhandri Yuhandri, Billy Hendrik
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