This work aims to address urgent concerns with IoT security and the approaches employed in big data analytics using literature research and an assessment of deep learning’s function in industrial automation settings. First, with an eye toward the integration of deep learning into industrial automation environments, research will examine existing IoT security issues and evaluate the big data analytics techniques used in past studies. This investigation will especially highlight where security solutions lack scalability and response to emerging security concerns. The study will next turn to investigate how IoT security reliability is affected by the mix of deep learning and big data analytics. The main objective of the research is to identify fundamental elements that enhance IoT network dependability in the face of high data flows using the security and resilience analysis of many technologies. Building on earlier research, this paper proposes a hybrid architecture for IoT security using deep learning to augment defenses for large volumes of data. Combining many security layers with strong machine learning algorithms in the suggested architecture would provide a more flexible and robust defense mechanism against complicated cyberattacks. Finally, to validate the hybrid model, research will contrast its processing speed and reliability with those of more traditional IoT security approaches. This test will evaluate the degree to which deep learning improvements provide more trustworthy and quick IoT security solutions, therefore determining the performance of the model in real-time data environments. By incorporating deep learning into this model, researchers seek to provide a safe system that can manage the vast volumes of data produced by these sensors and expand with IoT.