원문정보
초록
영어
In recent years, Distributed Denial of Service (DDoS) attacks have become increasingly frequent, posing serious threats to the security and stability of network systems. To enhance the effectiveness of DDoS detection, this paper proposes a deep learning model that integrates Convolutional Neural Networks (CNN) with a Transformer architecture to achieve efficient recognition of multiple types of attacks on the CIC-DDoS2019 dataset. By combining feature extraction and temporal modeling, the model fully captures both spatial and contextual information in network traffic, significantly improving detection accuracy and robustness. Experimental results demonstrate that the proposed method outperforms traditional CNN-based models across several sub-datasets, achieving higher accuracy, recall, and F1 scores while maintaining a favorable balance between training and inference time. This research offers new insights and technical support for developing efficient and scalable intelligent network defense systems.
목차
1. Introduction
1.1 Research Background
1.2 Research Objective
1.3 Organization of the Paper
2. Related Work
2.1 Overview of DDoS Attacks
2.2 Evolution of DDoS Detection: From Machine Learning to Deep Learning
2.3 Representative Deep Learning-Based DDoS Detection Studies
2.4 Transformer-CNN Combination Models with Attention Mechanisms and Innovations of This Study
3. The Proposed Method
3.1 Dataset Description
3.2 Model Architecture
3.3 Model Training
3.4 Performance Evaluation
4. Experiments and Results Analysis
4.1 Training Environment and Parameter Settings
4.2 Experimental Results
5. Conclusion and Future Work
5.1 Conclusion
5.2 Limitations
5.3 Future Research Directions
References