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Journals / Bilişim Teknolojileri Dergisi / 2026 / Cilt: 19 Sayı: 2

Optimized Ensemble Learning for DDoS Detection in Internet of Vehicles

Araçların İnternetinde DDoS Tespiti için Optimize Edilmiş Topluluk Öğrenmesi

Elif Özceylan(Istanbul Topkapı University)0000-0003-3216-1979
Şahsene Altınkaya(BEYKENT ÜNİVERSİTESİ)0000-0002-7950-8450
Sevim Bocekci(MARMARA ÜNİVERSİTESİ)0009-0000-2284-5256
Esra Yılmaz(MARMARA ÜNİVERSİTESİ)0000-0003-2411-4937
Sadettin Demir(MARMARA ÜNİVERSİTESİ)0000-0003-1875-6394
Şeyda Karcı(MARMARA ÜNİVERSİTESİ)0000-0001-8489-7828
Zeki Çıplak(GEDİK ÜNİVERSİTESİ, GEDİK MESLEK YÜKSEKOKULU)0000-0002-0086-3223
Kazım Yıldız(MARMARA ÜNİVERSİTESİ)0000-0001-6999-1410
Elif Nur Kucur(MARMARA ÜNİVERSİTESİ)0009-0005-1467-5599
Pages
125–145
Publication Date
2026-04-30
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

These results highlight the model’s reliability and efficiency in detecting DDoS attacks in dynamic IoV environments, overcoming key weaknesses of existing methods.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Distributed Denial of Service (DDoS) attacks remain a significant threat to network security, affecting service availability and integrity.
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Key Findings & Data:Traditional defenses, including anomaly detection, signature-based methods, and machine learning (ML) approaches, face challenges such as high false-positive rates, poor scalability, and limited generalization to new attack patterns.
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Conclusion & Impact:These results highlight the model’s reliability and efficiency in detecting DDoS attacks in dynamic IoV environments, overcoming key weaknesses of existing methods.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:DDoS saldırısı · Toplu öğrenme · Yumuşak oylama

Abstract

Distributed Denial of Service (DDoS) attacks remain a significant threat to network security, affecting service availability and integrity. Traditional defenses, including anomaly detection, signature-based methods, and machine learning (ML) approaches, face challenges such as high false-positive rates, poor scalability, and limited generalization to new attack patterns. Anomaly detection often generates excessive false positives, while signature-based methods fail to detect novel attacks. Conventional ML techniques struggle to capture the complexity of evolving network traffic, reducing their effectiveness in real-world scenarios. To address these limitations, this study proposes an ensemble intrusion detection model combining Gradient Boosting, AdaBoost, and Naive Bayes with optimized classifier weights and a soft voting mechanism. Using the CICIoV2024 dataset, tailored for Internet of Vehicles (IoV) scenarios, the ensemble outper forms individual models with F1-score values of 0.96, 0.96, 0.98, and 0.97 for accuracy, recall, and precision, respectively. These results highlight the model’s reliability and efficiency in detecting DDoS attacks in dynamic IoV environments, overcoming key weaknesses of existing methods.

Özet

Dağıtılmış Hizmet Reddi (DDoS) saldırıları, hizmet kullanılabilirliğini ve bütünlüğünü etkileyerek ağ güvenliği için önemli bir tehdit oluşturmaya devam etmektedir. Anomali tespiti, imza tabanlı yöntemler ve makine öğrenimi (ML) yaklaşımları da dahil olmak üzere geleneksel savunmalar, yüksek yanlış pozitif oranları, düşük ölçeklenebilirlik ve yeni saldırı modellerine sınırlı genelleme gibi zorluklarla karşı karşıyadır. Anomali tespiti genellikle aşırı yanlış pozitif sonuçlar üretirken, imza tabanlı yöntemler yeni saldırıları tespit etmekte başarısız olur. Geleneksel ML teknikleri, gelişen ağ trafiğinin karmaşıklığını yakalamakta zorlanır ve bu da gerçek dünya senaryolarındaki etkinliklerini azaltır. Bu sınırlamaları gidermek için bu çalışma, Gradient Boosting, AdaBoost ve Naive Bayes'i optimize edilmiş sınıflandırıcı ağırlıkları ve yumuşak oylama mekanizmasıyla birleştiren bir topluluk saldırı tespiti modeli önermektedir. Araçların İnterneti (IoV) senaryolarına göre uyarlanmış CICIoV2024 veri setini kullanan topluluk, sırasıyla doğruluk, geri çağırma ve kesinlik açısından 0,96, 0,96, 0,98 ve 0,97'lik F1 puanı değerleriyle bireysel modellerden daha iyi sonuçlar elde ediyor. Bu sonuçlar, modelin dinamik IoV ortamlarında DDoS saldırılarını tespit etmedeki güvenilirliğini ve verimliliğini vurgulayarak, mevcut yöntemlerin temel zayıflıklarını ortadan kaldırıyor.

🔖

Cite This Article

Özceylan, E., Altınkaya, Ş., Bocekci, S., Yılmaz, E., Demir, S., Karcı, Ş., Çıplak, Z., Yıldız, K., Kucur, E. N. (2026). Optimized Ensemble Learning for DDoS Detection in Internet of Vehicles. Bilişim Teknolojileri Dergisi, Cilt: 19 Sayı: 2, 125–145. https://doi.org/10.17671/gazibtd.1801705

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