A novel approach to intrusion detection system using hybrid flower pollination and cheetah optimization algorithm.

The study aims to address critical challenges in network security, particularly the limitations of traditional intrusion detection systems (IDS) in terms of adaptability, detection precision, and high false positive rates in dynamic network environments. A novel hybrid IDS model integrating the Flower Pollination Algorithm (FPA), Cheetah Optimization Algorithm (COA), and Artificial Neural Networks (ANN) is proposed to enhance detection accuracy, reduce false positives, and optimize feature selection, anomaly detection, and rule adaptation. The hybrid FPA-COA-ANN model combines the optimization capabilities of FPA and COA with the predictive power of ANN. The model was evaluated using five benchmark datasets-CICIDS-2017, TII-SSRC, Lu-flow, NSL-KDD, and WSN-DS. Key performance metrics were analysed to assess the model's effectiveness in detecting malicious activities in complex network traffic patterns. The hybrid model demonstrated superior performance compared to existing IDS approaches. It achieved accuracy rates of 0.99 on CICIDS-2017, 1.00 on TII-SSRC, 1.00 on Lu-flow, 0.99 on NSL-KDD, and 0.93 on WSN-DS. The results highlight significant improvements in detection precision and adaptability, alongside a reduction in false positive rates, showcasing the model's robustness and scalability for real-time threat detection. The proposed hybrid FPA-COA-ANN model effectively mitigates the limitations of traditional IDS by offering a robust, scalable, and efficient solution for real-time network threat detection. Its high accuracy and adaptability across diverse benchmark datasets underscore its potential as a critical tool for enhancing cybersecurity defences in dynamic and complex environments.