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020 _a9783031540387
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024 7 _a10.1007/978-3-031-54038-7
_2doi
050 4 _aQ342
072 7 _aUYQ
_2bicssc
072 7 _aCOM004000
_2bisacsh
072 7 _aUYQ
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245 1 0 _aMachine Learning for Cyber Physical System: Advances and Challenges
_h[electronic resource] /
_cedited by Janmenjoy Nayak, Bighnaraj Naik, Vimal S, Margarita Favorskaya.
250 _a1st ed. 2024.
264 1 _aCham :
_bSpringer Nature Switzerland :
_bImprint: Springer,
_c2024.
300 _aXVI, 406 p. 148 illus., 109 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
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_2rda
490 1 _aIntelligent Systems Reference Library,
_x1868-4408 ;
_v60
505 0 _aSMOTE 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.
520 _aThis 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. .
650 0 _aComputational intelligence.
_964052
650 0 _aCooperating objects (Computer systems).
_964303
650 0 _aMachine learning.
_955059
650 0 _aArtificial intelligence.
_958441
650 1 4 _aComputational Intelligence.
_964052
650 2 4 _aCyber-Physical Systems.
_965638
650 2 4 _aMachine Learning.
_955059
650 2 4 _aArtificial Intelligence.
_958441
700 1 _aNayak, Janmenjoy.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
_975030
700 1 _aNaik, Bighnaraj.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
_975031
700 1 _aS, Vimal.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
_975032
700 1 _aFavorskaya, Margarita.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
_975033
710 2 _aSpringerLink (Online service)
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783031540370
776 0 8 _iPrinted edition:
_z9783031540394
776 0 8 _iPrinted edition:
_z9783031540400
830 0 _aIntelligent Systems Reference Library,
_x1868-4408 ;
_v60
_967034
856 4 0 _uhttps://doi.org/10.1007/978-3-031-54038-7
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