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Distributed Denial of Service (DDoS) attacks pose a significant threat to thestability and reliability of online systems. Effective and early detection ofsuch attacks is pivotal for safeguarding the integrity of networks. In thiswork, we introduce an enhanced approach for DDoS attack detection by leveragingthe capabilities of Deep Residual Neural Networks (ResNets) coupled withsynthetic oversampling techniques. Because of the inherent class imbalance inmany cyber-security datasets, conventional methods often struggle with falsenegatives, misclassifying subtle DDoS patterns as benign. By applying theSynthetic Minority Over-sampling Technique (SMOTE) to the CICIDS dataset, webalance the representation of benign and malicious data points, enabling themodel to better discern intricate patterns indicative of an attack. Our deepresidual network, tailored for this specific task, further refines thedetection process. Experimental results on a real-world dataset demonstratethat our approach achieves an accuracy of 99.98%, significantly outperformingtraditional methods. This work underscores the potential of combining advanceddata augmentation techniques with deep learning models to bolstercyber-security defenses.

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