DDoSGedik30K: DDoS Saldırı Tespiti için Benzersiz Bir Veri Seti ve Gelişmiş Derin Öğrenme Teknikleri

Öz

Ağ teknolojilerindeki hızlı gelişmeler ve ağlar üzerinde aktarılan verilerin miktarı ve kapsamı her geçen gün artmaktadır. Bu duruma bağlı olarak siber tehdit ve saldırıların yoğunluğu ve karmaşıklığı da genişlemektedir. Özellikle DDoS (Distributed Denial of Service) saldırıları, ağ ve sistem güvenliğini tehdit eden en yaygın ve yıkıcı siber saldırı türlerinden biri olarak dikkat çekmektedir. Bu çalışma, günümüzde giderek artan DDoS saldırılarına karşı etkili bir tespit mekanizması geliştirmek amacıyla derin öğrenme tekniklerinin kullanımını incelemektedir. Bu amaçla, gerçek dünya saldırı senaryolarını içeren DDoSGedik30K adlı bir veri seti oluşturulmuştur. Bu veri seti kullanılarak, Feedforward Neural Network (FFNN) ve Long Short-Term Memory (LSTM) derin öğrenme mimarilerinde toplam 12 model geliştirilmiştir. Geliştirilen tüm modellerin %99.9 doğruluk oranına ulaşması, önerilen veri setinin DDoS saldırılarını tespit etmede son derece etkili olduğunu ispat etmiştir. Çalışmada önerilen veri seti ve DDoS saldırı tespiti için optimize edilmiş derin öğrenme modelleri, literatüre önemli bir katkı sağlamaktadır ve gelecekteki siber güvenlik araştırmaları için yol gösterici olabilecek bulgular sunmaktadır.

Anahtar Kelimeler

• DDoS Algılama
• Derin Öğrenme
• DDoS Veri Seti
• DDoSGedik30K
• DDoS Analizi

DDoSGedik30K: A Unique Dataset and Advanced Deep Learning Techniques for DDoS Attack Detection

Abstract

The rapid advancements in network technologies, along with the increasing volume and scope of data transmitted over networks, have led to a rise in both the intensity and complexity of cyber threats and attacks. One of the most prominent and destructive types of cyberattacks threatening network and system security is the Distributed Denial of Service (DDoS) attack. This study examines the use of deep learning techniques to develop an effective detection mechanism against the growing number of DDoS attacks today. For this purpose, a dataset called DDoSGedik30K, which includes real-world attack scenarios, was created. Using this dataset, a total of 12 models were developed based on Feedforward Neural Network (FFNN) and Long Short-Term Memory (LSTM) deep learning architectures. The fact that all models achieved a 99.9% accuracy rate proves that the proposed dataset is highly effective in detecting DDoS attacks. The dataset and the optimized deep learning models for DDoS attack detection proposed in this study provide a significant contribution to the literature and offer findings that could guide future cybersecurity research.

Keywords

• DDoS Detection
• Deep Learning
• DDoS Dataset
• DDoSGedik30K
• DDoS Analysis.

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