Cyber attack detection in monitoring on optoelectronics devices using deep learning model and cloud computing network

In light of recent advances in optical computing and machine learning, we examine the scenarios in which all-optical computing might surpass electrical and optoelectronic computing with regards to energy efficiency and scalability. When assessing the overall performance of a system, the expense of memory access and data collecting is likely to be a significant bottleneck that affects not just electrical but also optoelectronic and all-optical implementations. The study's focus on cloud-based deep learning models for monitoring optoelectronic devices is meant to pave the way for fresh approaches of detecting cyber attacks. In this setup, optoelectronic devices communicate through cloud-based systems. After that, a Gaussian regressive discriminator based transfer learning model keeps an eye on the system to see whether it's under cyber assault. The experimental analysis is carried out for various cyber-attacks dataset in terms of scalability, robustness, average accuracy, precision, network security. We come to a conclusion based on this work by outlining difficulties and areas for future-proof optical cloud system research. Proposed technique attained Average Accuracy 98%, Precision of 97%, network security of 96%, scalability of 95%.