Hybrid Deep Learning Approach for Intelligent Cyber Threat Detection and Access Control in Organizations

The increasing complexity of cyber threats poses significant challenges to organizational security, particularly in environments with sensitive data and multiple access points. Traditional security methods often struggle to detect sophisticated attacks and manage dynamic access requirements efficiently. This study proposes a hybrid deep learning framework that integrates multiple neural network models to enhance both threat detection and access control in organizational networks. The framework leverages feature extraction and sequence learning to identify anomalous activities in real time while enforcing adaptive access policies based on user behavior and contextual information. Experimental evaluation using simulated organizational network data demonstrates that the proposed model achieves high accuracy in detecting threats and efficiently regulates access privileges, outperforming conventional methods. The findings suggest that hybrid deep learning can provide a robust and intelligent solution for securing modern organizational infrastructures.