Hybrid technique to detect network intrusion using a deep learning algorithm in RNN – a elaborative review

The main objective of this research is to develop a deep learning-based method that enhances the accuracy and efficiency of detecting network intrusions from known malicious sources. Unlike conventional intrusion detection systems (IDS), which often struggle with identifying complex or novel attack patterns, this hybrid approach leverages the capabilities of Recurrent Neural Networks (RNNs) to model sequential data and temporal behaviors of network traffic. The system is designed to provide detailed information about the type, intensity, and target of potential attacks, enabling organizations to implement more strategic cyber security defenses. The study involves the analysis of existing deep learning algorithms for intrusion detection, collection of real-world network traffic data, and the development of a secure, robust, and accurate IDS framework. The proposed hybrid technique focuses on improving system integrity and minimizing false positives to ensure effective detection and mitigation of network threats.To study and analyze the existing algorithm of deep learning and technology of intrusion detection systemData collection regarding network attacks from molecules sourcesTo determine how to improve security parametersTo implements secure system in terms of integrity and accuracy