Hybrid Optimization-Based DDoS Attack Detection Using Fireworks and Grey Wolf Algorithms with Deep Learning

The rapid expansion of Internet of Things (IoT) technologies has introduced significant security issues requiring advanced Intrusion Detection Systems (IDS) to counteract changing threats. Constructing a robust system to prevent such attacks is critical. Prior work primarily relies on traditional feature selection techniques for training models, frequently disregarding the application of hybrid approaches. In this paper, a novel hybrid algorithm is proposed to improve the performance of machine learning (ML) and deep learning (DL) models in detecting attacks within anomaly intrusion detection systems. The hybrid algorithm leverages Fireworks and Grey Wolf optimization algorithms for feature selection, enabling the models to prioritize the most significant features during training, thereby improving predictive accuracy. The results revealed that the random forest outperformed other models achieving a recall of 99.75%, highlighting the proposed model's capability to accurately detect and classify the incoming threats.