Abstract
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%.
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BL: Conceptualization, Methodology, Software, Data curation, Writing- Original, XW: draft preparation, Visualization, Investigation, Supervision, Software, Validation, Writing- Reviewing and Editing.
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Liu, B., Wang, X. Cyber attack detection in monitoring on optoelectronics devices using deep learning model and cloud computing network. Opt Quant Electron 55, 1297 (2023). https://doi.org/10.1007/s11082-023-05554-6
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DOI: https://doi.org/10.1007/s11082-023-05554-6