Advancing DDoS Attack Detection: A Synergistic Approach Using Deep Residual Neural Networks and Synthetic Oversampling

Distributed Denial of Service (DDoS) attacks pose a significant threat to the stability and reliability of online systems. Effective and early detection of such attacks is pivotal for safeguarding the integrity of networks. In this work, we introduce an enhanced approach for DDoS attack detection by leveraging the capabilities of Deep Residual Neural Networks (ResNets) coupled with synthetic oversampling techniques. Because of the inherent class imbalance in many cyber-security datasets, conventional methods often struggle with false negatives, misclassifying subtle DDoS patterns as benign. By applying the Synthetic Minority Over-sampling Technique (SMOTE) to the CICIDS dataset, we balance the representation of benign and malicious data points, enabling the model to better discern intricate patterns indicative of an attack. Our deep residual network, tailored for this specific task, further refines the detection process. Experimental results on a real-world dataset demonstrate that our approach achieves an accuracy of 99.98%, significantly outperforming traditional methods. This work underscores the potential of combining advanced data augmentation techniques with deep learning models to bolster cyber-security defenses.

分布式拒绝服务（DDoS）攻击对在线系统的稳定性和可靠性构成重大威胁。有效且尽早地检测此类攻击对于保护网络的完整性至关重要。在这项工作中，我们通过利用深度残差神经网络（ResNets）的能力并结合合成过采样技术，引入了一种增强的DDoS攻击检测方法。由于许多网络安全数据集中存在固有的类别不平衡，传统方法经常出现假阴性问题，将细微的DDoS模式误分类为良性。通过对CICIDS数据集应用合成少数类过采样技术（SMOTE），我们平衡了良性和恶意数据点的表示，使模型能够更好地识别表明攻击的复杂模式。我们针对这一特定任务定制的深度残差网络进一步优化了检测过程。在一个真实世界数据集上的实验结果表明，我们的方法达到了99.98%的准确率，显著优于传统方法。这项工作强调了将先进的数据增强技术与深度学习模型相结合以加强网络安全防御的潜力。