Cybersecurity is a paramount concern in both Internet of Things (IoT) and Cyber-Physical Systems (CPSs) due to the interconnected and often critical nature of these systems. The integration of AI ML into the realm of IoT and CPS security has gained significant attention and momentum in recent years. The success of AI ML in various domains has sparked interest in leveraging these technologies to enhance the security, resilience, and adaptability of IoT and CPS. Secure and Smart Cyber-Physical Systems provides an extensive exploration of AI ML-based security applications in the context of IoT and CPS. One of the most popular revolutions of technology is the Cyber-Physical System (CPS). CPS is an integration of cyber world (computation and communication systems) and man-made physical world (e.g., utility networks, vehicles, and factories.) formed by using sensors and actuators. Cyber systems make the physical infrastructures smarter, more secure, and reliable, and fully automated systems foster a more efcient, resilient, and sustainable built environment. In the near future (industry 4.0 revolution or 4IR), CPSs will become the new "techno-economic" paradigm. DL model consists of multiple layers of artifcial neural networks (ANNs) that are trained using supervised or unsupervised learning. Python libraries are blocks of code that contain built-in functions. These libraries provide access to the necessary packages or modules that can be installed to complete specifc tasks. By providing data operations and data structures, the Pandas' library signifcantly aids in the study and manipulation of data, particularly time series and numerical tables. On top of NumPy, the Pandas' package was developed. It supports the efective implementation of a data frame. Series and data frames are the foundational data structures on which Pandas is built. Pandas allow for the transformation of data structures into data frame objects, handling of missing data, and histogram or box plots.