Next-Generation Intrusion Detection Systems: A Hybrid Machine Learning Framework for Intelligent Cyber Threat Neutralization

In the rapidly evolving landscape of cybersecurity, network intrusion detection systems (NIDS) face significant challenges in effectively identifying and mitigating sophisticated cyber threats. In this research, we propose an innovative hybrid approach that combines signature-based detection, anomaly-based detection and LSTM model as a substantial solution to the limitations faced in existing intrusion detection methodologies. The hybrid intrusion detection system is a game changer in threat detection capabilities. The research addresses the inherent weaknesses of traditional single-method approaches by combining multiple detection methodologies. Signature-based detection works well for known threats but is ineffective against zero-day attacks, and anomaly-based detection produces high false positive rates. This innovative hybrid model utilizes machine learning as a smart filtering mechanism to fill these crucial voids. Extensive simulations and in depth statistical analysis show impressive performance gains. The system attained a true positive detection rate of 98% which is a significant improvement over previous methods whilst reducing the final false positive rates by approximately 70%. The performance metrics define the efficiency of the system with 98% detection accuracy, significant reduction in false positive rates and increased threat recognition at known and unknown attack surface. The proposed system integrates a variety of detection methods along with refined machine learning approaches to provide an overall intelligent and adaptive network security framework, making it a strong candidate for advanced intrusion detection systems.