Enhanced DDoS Attack Detection Using Shepard Interpolation Neural Network with Artificial Rabbit Optimizer and SMOTE Balancing on Kaggle Dataset
Srinivas Cheekati, Ramya Vani Rayala, Chandrakanth Reddy Borra, G K Madhura
Abstract
Strong defence mechanisms are necessary to defend the availability and integrity of network infrastructure in the ever-changing cybersecurity landscape, where Distributed Denial of Service (DDoS) attacks are becoming more common. The capacity of Deep Learning (DL) models to automatically learn feature representations and discern complicated patterns within network traffic data has made them a potential strategy for DDoS attack finding and mitigation. The design of a critical cybersecurity threat that disrupts network operations and causes considerable economic losses internationally also affects how well DL models fight against developing attacks. The Shepard Interpolation Neural Network (SINN) classifier, fine-tuned with the Artificial Rabbit Optimiser (ARO), is proposed as a new method for DDoS assault detection in this paper. The technique takes advantage of the Kaggle DDoS dataset, which is naturally skewed and poses problems for conventional ML models. The solution is to use the Synthetic Minority Oversampling Technique (SMOTE) to level the playing field in the dataset, which will result in better model presentation in every class. Thanks to ARO’s hyperparameter optimisation, the SINN classifier—which is already well-known for its interpolation capabilities and resilience when dealing with nonlinear patterns—achieves even better detection accuracy. Experiments show that the suggested model outdoes state-of-the-art machine learning tactics for DDoS attack finding in terms of accuracy, recall, and F1-score. Reliable classification results are contributed to by the incorporation of SMOTE, which successfully mitigates bias induced by class imbalance. In addition to establishing a scalable framework for practical applications, this work showcases SINN-ARO’s capabilities in protecting networks from ever-changing cyber threats. Investigating how well this approach works in real-time, dynamic network settings and expanding its applicability to identify different forms of cyberattacks are the primary goals of future studies.