04831nam a22006015i 4500 978-3-031-54038-7 DE-He213 20251030111814.0 cr nn 008mamaa 240411s2024 sz | s |||| 0|eng d 9783031540387 978-3-031-54038-7 10.1007/978-3-031-54038-7 doi Q342 UYQ bicssc COM004000 bisacsh UYQ thema Machine Learning for Cyber Physical System: Advances and Challenges [electronic resource] / edited by Janmenjoy Nayak, Bighnaraj Naik, Vimal S, Margarita Favorskaya. 1st ed. 2024. Cham : Springer Nature Switzerland : Imprint: Springer, 2024. XVI, 406 p. 148 illus., 109 illus. in color. online resource. text txt rdacontent computer c rdamedia online resource cr rdacarrier text file PDF rda Intelligent Systems Reference Library, 1868-4408 ; 60 SMOTE Integrated Adaptive Boosting Framework for Network Intrusion Detection -- An In-depth Analysis of Cyber-Physical Systems: Deep Machine Intelligence based Security Mitigations -- Unsupervised approaches in anomaly detection -- Profiling and Classification of IoT Devices for Smart Home Environments -- Application of Machine Learning to Improve Safety in the Wind Industry -- Malware Attack Detection in Vehicle Cyber Physical System for Planning and Control using Deep Learning -- Unraveling what is at stake in the intelligence of autonomous cars -- Intelligent Under-Sampling based Ensemble Techniques for Cyber-Physical Systems in Smart Cities -- Application of Deep Learning in Medical Cyber-Physical Systems -- Risk Assessment and Security of Industrial Internet of Things Network using Advance Machine Learning -- Machine Learning Based Intelligent Diagnosis of Brain Tumor: Advances and Challenges -- Cyber-Physical Security in Smart Grids: A Holistic View with Machine Learning Integration -- Intelligent Biometric Authentication-based Intrusion Detection in Medical Cyber Physical System using Deep Learning -- Current datasets and their inherent challenges for Automatic Vehicle Classification. This book provides a comprehensive platform for learning the state-of-the-art machine learning algorithms for solving several cybersecurity issues. It is helpful in guiding for the implementation of smart machine learning solutions to detect various cybersecurity problems and make the users to understand in combating malware, detect spam, and fight financial fraud to mitigate cybercrimes. With an effective analysis of cyber-physical data, it consists of the solution for many real-life problems such as anomaly detection, IoT-based framework for security and control, manufacturing control system, fault detection, smart cities, risk assessment of cyber-physical systems, medical diagnosis, smart grid systems, biometric-based physical and cybersecurity systems using advance machine learning approach. Filling an important gap between machine learning and cybersecurity communities, it discusses topics covering a wide range of modern and practical advance machine learning techniques, frameworks, and development tools to enable readers to engage with the cutting-edge research across various aspects of cybersecurity. . Computational intelligence. 64052 Cooperating objects (Computer systems). 64303 Machine learning. 55059 Artificial intelligence. 58441 Computational Intelligence. 64052 Cyber-Physical Systems. 65638 Machine Learning. 55059 Artificial Intelligence. 58441 Nayak, Janmenjoy. editor. edt http://id.loc.gov/vocabulary/relators/edt 75030 Naik, Bighnaraj. editor. edt http://id.loc.gov/vocabulary/relators/edt 75031 S, Vimal. editor. edt http://id.loc.gov/vocabulary/relators/edt 75032 Favorskaya, Margarita. editor. edt http://id.loc.gov/vocabulary/relators/edt 75033 SpringerLink (Online service) Springer Nature eBook Printed edition: 9783031540370 Printed edition: 9783031540394 Printed edition: 9783031540400 Intelligent Systems Reference Library, 1868-4408 ; 60 67034 https://doi.org/10.1007/978-3-031-54038-7 ZDB-2-INR ZDB-2-SXIT 59078 59078