Intrusion-Detection System Using Hybrid ML Models

Intrusion Detection System(IDS) plays a pivotal role in safeguarding modern networks from the everevolving landscape of cyber threats. Traditional IDS approaches often face challenges such as high false positive rates, poor scalability, and inability to detect novel attacks. To address these limitations, hybrid (ML) models have gained attention as an effective solution by integrating multiple algorithms to improve detection accuracy and adaptability. This research explores the development of a hybrid ML-based IDS that leverages advanced feature extraction techniques and ensemble learning frameworks. The proposed model is evaluated on benchmark datasets such as NSL-KDD, UNSW-NB15, and CICIDS2017, achieving significant improvements in detection accuracy, precision, and runtime performance over state-of-the-art methods. This research highlights the insights into the integration of hybrid ML models for robust, scalable, and efficient intrusion detection, highlighting their potential for deployment in real-world network environments.