ENHANCED DDoS ATTACK DETECTION THROUGH HYBRID MACHINE LEARNING TECHNIQUES

OpenAlex İngilizce

This research focuses on enhancing the detection mechanisms for Distributed Denial of Service (DDoS) attacks using advanced machine learning techniques. We explore two innovative approaches: a metaclassifier stacking model and a transfer learning model, utilizing the CICDDoS2019 and CICIDS2017 datasets for training and evaluation. The first approach integrates K-Nearest Neighbors (KNN), Support Vector Machine (SVM), and Random Forest (RF) algorithms through a logistic regression metaclassifier. This ensemble method harnesses the strengths of each algorithm, leading to improved metrics such as accuracy, precision, recall, and F1-score. The second approach employs transfer learning, where a pre-trained Artificial Neural Network (ANN) on the CICIDS2017 dataset is fine-tuned with the CICDDoS2019 dataset. This technique demonstrates the benefits of knowledge transfer, achieving high detection performance with reduced training time. Our findings reveal that both methods significantly enhance DDoS detection. The metaclassifier approach delivers superior performance metrics but requires more computational resources. In contrast, the transfer learning approach provides an efficient balance between performance and computational demand, ideal for rapid deployment scenarios. In summary, this study highlights the efficacy of combining multiple algorithms and leveraging pre-trained models to improve DDoS detection accuracy and efficiency. These approaches offer promising directions for developing robust and effective DDoS detection systems.