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Full Description
This book offers a comprehensive analysis of the significant intersection where human factors and cyber-physical system (CPS) reliability meet. Physical component integration has become essential in a number of industries, including smart infrastructure, health care, transportation, and manufacturing. However, human performance and decision-making inside these complex frameworks also play an important part in determining the reliability of CPS. Key subjects discussed include the role of human factors in CPS reliability, machine learning and deep learning applications in cybersecurity, resilience engineering, cognitive task management, efficient team collaboration and communication, error control, and cybersecurity awareness.
The book will be read by professionals and scholars working in engineering, human factors, reliability engineering, cybersecurity, and related topics. In order to obtain a greater knowledge of the crucial role that human factors play in providing the reliability and trustworthiness of CPS, it is also beneficial for students pursuing courses or research in CPS, human-computer interface (HCI), and systems engineering.
Contents
Recurrent integrated CNN gate (RICG): A dynamic deep learning model for security and efficiency enhancement in cyber-physical systems.- A cognitive workload-aware machine learning model for performance enhancement in cyber-physical systems.- Audio driven detection of hate speech in Telugu: Toward ethical and secure CPS.- Vision transformer-based audio analysis for depression detection: A human factor in reliable CPS.- A distributed approach based on Catboost, BlockChain and edge computing for IoT security.- An improved anomaly detection based on ensemble learning and deep Q-network for mobile edge computing monitoring.- Efficient anomaly detection for cyber-physical leveraging knowledge distillation and model quantization.- Interpretable anomaly detection for cyber-physical system risk mitigation using CNN and SHAP.- Intrusion detection approaches in healthcare systems: An overview.- Optimizing intrusion detection systems: A machine learning-based feature selection approach for enhanced cybersecurity.- Human factors in cyber-physical systems: Bridging the gap between humans and technology.- Ensemble-based cognitive IDS for IIoT in cyber-physical environments.- Towards reliable and secure IoMT: A deep learning perspective on cyber-physical threats.- Improved computational diffie-hellman-based mechanism for cyber-physical security.- Enhancing PE malware detection: A comparative study of feature-based and image-based representations.- A lightweight attention-enhanced deep learning framework for malware detection in IoT: A comparative study of structured and image-based data representations.- Machine learning for cyber-physical systems: A short survey.- Formal methods for cyber-physical systems.
