• Anuradha Rai
• Mr. Darvesh Akhtar

Cyber-physical systems (CPS), including but not limited to smart grids, autonomous vehicles, industrial automation, and IoT networks, leverage artificial intelligence for efficient, effective, and secure operation. Traditional deep learning models have been successfully applied in CPS, but they face difficulties operationalizing in CPS contexts due to the diverse variety of data and pure dimensions of data, low computing power, continuous unpredictable changes in their surroundings, and the dire need to mitigate security threats. To mitigate these challenges, researchers have started to adopt hybrid deep learning models; these include combinations of different neural network architectures (e.g., CNN-RNN, GNN-Transformers, Autoencoder-GANs) and different technologies (federated learning, blockchain technology, edge computing). Hybrid deep learning models show to have better sophistication, scale and resilience to tackle CPS applications. In the survey, we review peer-reviewed research articles from January 2015 to March 2025, and focus on, but not limited to, hybrid models that can explain how the approaches have progressed through knowledge transfer across tasks (i.e. traffic forecasting, anomaly detection, occupational accident risk and predictive maintenance). We present a taxonomy that classifies the various hybrid models that examines CPS contexts based on their architecture, learning traffic management strategies, and employed security mechanisms. A panel of experts on hybrid deep learning discussed the state of CPS by performing meta-assesment study, and since 2015, we show that hybrid models produce a statistically significant 4-12% multitarget predicted performance over equivalent single -target models across a range of settings, including: healthcare, energy, and manufacturing. Additionally, we briefly reviewed current headwinds and opportunities facing researchers employing hybrid models on CPS utilization such as: data imbalance, concept drift, and privacy concerns and solutions such as self-supervised learning (e.g. multi-armed bandit) and explainable AI. Lastly, we conclude with several future directions for hybrid deep learning researchers that we hope will help form the basis for CPS researchers- developing intelligent, secure, and scalable applications.

Explainable Deep Learning, Interpretability, Transparency, Black-box Models, Model-specific Techniques, Model-agnostic Methods, Saliency Maps, SHAP, LIME.

"Hybrid Deep Learning Paradigms for Secure and Intelligent Cyber–Physical Systems: A Comprehensive Survey ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 8, page no.f158-f165, August-2025, Available :http://www.jetir.org/papers/JETIR2508522.pdf

"Hybrid Deep Learning Paradigms for Secure and Intelligent Cyber–Physical Systems: A Comprehensive Survey ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 8, page no. ppf158-f165, August-2025, Available at : http://www.jetir.org/papers/JETIR2508522.pdf