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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.
CPS have become ubiquitous and the core of modern critical infrastructure and industrial applications in recent years. CPSs such as self-driving cars, drones, and intelligent transportation rely heavily on machine learning techniques for ever increasing levels of autonomy. Further, the deployed sensors generate a massive amount of real-time Big data from the physical infrastructure and send it to the cyber systems using communication infrastructure (such as switches and routers). In turn, the cyber systems also send feedback to the physical devices using the communication infrastructure.
On the other hand, these systems provide an appeal to attackers. Cybersecurity is, thus, of prime concern in CPSs. Due to the success of Deep Learning (DL) in a multitude of domains, the development of DL-based CPS security applications has received increased interest in the past few years. However, despite the broad body of work on using DL for ensuring the security of CPSs, to our best knowledge, very little work exists where the focus is on the development of these DL applications. DL based on artifcial neural networks is a very popular approach to modeling, classifcation, and the recognition of complex data including images, voice, and text.
DL model consists of multiple layers of artifcial neural networks (ANNs) that are trained using supervised or unsupervised learning. DL which is a subset of ML can be classifed into three categories: i) Generative models that are used for unsupervised learning, ii) discriminative models that are used for supervised learning, and iii) the hybrid models that can be classifed as semi-supervised learning. The diferent DL models are discussed briefy in this chapter.
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. A quick explanation of the libraries used in the framework is provided in this section. (1) Pandas: 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. Data frames are a two-dimensional structure in the form of a table with several columns, whereas series are a one-dimensional structure in the form of a list of items. Row and column labels for homogeneous and heterogeneous data types with or without missing data make up data frames. Pandas allow for the transformation of data structures into data frame objects, handling of missing data, and histogram or box plots. (2) Scikit-learn: The collection of various classifcation, grouping, and regression algorithms in the Scikit-learn package helps learning and decision-making. It is a Python module built on top of the machine learning library SciPy. A widely used software for Python-based data science applications is called Scikit-learn...
Preface
1 Machine Learning and Deep Learning Approaches in Cyber-Physical Systems
2 Securing Cyber-Physical Systems Using Artifcial Intelligence
3 Toward Fast Reliable Intelligent Industry 5.0—A Comprehensive Study
4 Software Development for Medical Devices: A Comprehensive Guide
5 6G Communication Technology for Industry 5.0: Prospect, Opportunities, Security Issues, and Future Directions
6 Cyber-Physical System in AI-Enabled Smart Healthcare System
7 Service-Oriented Distributed Architecture for Sustainable Secure Smart City
8 A Comprehensive Security Risk Analysis of Clif Edge on Cyber-Physical Systems
9 Securing Financial Services with Federated Learning and Blockchain