  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.
