Design Thinking –An Effective Method for Enhancing Intrusion Detection Systems through Hyper parameter Tuning in Deep Learning

This research study explores the optimization of Intrusion Detection Systems (IDS) by fine-tuning hyperparameters for Deep Learning (DL) algorithms, incorporating data analytics. The research systematically analyzes the impact of hyperparameters on DL models, including CNNs, RNNs, and their variants. Diverse datasets are utilized to evaluate the robustness and versatility of the tuned models. The results demonstrate substantial improvements in IDS accuracy and efficiency, emphasizing the significance of hyperparameter tuning and data analytics. This research provides valuable insights for optimizing IDS performance, advancing network security in the face of evolving cyber threats.