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020 _a9789819921157
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024 7 _a10.1007/978-981-99-2115-7
_2doi
050 4 _aQ334-342
050 4 _aTA347.A78
072 7 _aUYQ
_2bicssc
072 7 _aCOM004000
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072 7 _aUYQ
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082 0 4 _a006.3
_223
245 1 0 _aArtificial Intelligence and Cyber Security in Industry 4.0
_h[electronic resource] /
_cedited by Velliangiri Sarveshwaran, Joy Iong-Zong Chen, Danilo Pelusi.
250 _a1st ed. 2023.
264 1 _aSingapore :
_bSpringer Nature Singapore :
_bImprint: Springer,
_c2023.
300 _aVIII, 373 p. 100 illus., 86 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aAdvanced Technologies and Societal Change,
_x2191-6861
505 0 _aIntroduction to Artificial Intelligence and Cyber Security for Industry -- Role of AI and its impact on the development of cyber security applications -- AI and IoT in Manufacturing and related Security Perspectives for Industry 4.0 -- IoT Security Vulnerabilities and Defensive Measures in Industry 4.0 -- Adopting Artificial Intelligence in ITIL for Information Security Management - Way forward in Industry 4.0 -- Intelligent Autonomous Drones in Industry 4.0 -- A review on automatic generation of attack trees and its application to automotive cybersecurity -- Malware Analysis using Machine Learning Tools and Techniques in IT Industry -- USE OF MACHINE LEARNING IN FORENSICS AND COMPUTER SECURITY -- Control of feed drives in CNC machine tools using artificial immune adaptive strategy -- Efficient Anomaly Detection for Empowering Cyber Security by Using Adaptive Deep Learning Model -- Intrusion Detection in IoT based Healthcare Using ML and DL approaches: A Case Study -- War Strategy Algorithm based GAN model for Detecting the Malware Attacks in Modern Digital Age -- ML algorithms for providing financial security in banking sectors with the prediction of loan risks -- Machine Learning based DDoS Attack Detection using Support Vector Machine -- Artificial Intelligence based Cyber Security Applications.
520 _aThis book provides theoretical background and state-of-the-art findings in artificial intelligence and cybersecurity for industry 4.0 and helps in implementing AI-based cybersecurity applications. Machine learning-based security approaches are vulnerable to poison datasets which can be caused by a legitimate defender's misclassification or attackers aiming to evade detection by contaminating the training data set. There also exist gaps between the test environment and the real world. Therefore, it is critical to check the potentials and limitations of AI-based security technologies in terms of metrics such as security, performance, cost, time, and consider how to incorporate them into the real world by addressing the gaps appropriately. This book focuses on state-of-the-art findings from both academia and industry in big data security relevant sciences, technologies, and applications.
650 0 _aArtificial intelligence.
650 0 _aInternet of things.
650 0 _aBig data.
650 0 _aMachine learning.
650 0 _aComputational intelligence.
650 0 _aWireless communication systems.
650 0 _aMobile communication systems.
650 1 4 _aArtificial Intelligence.
650 2 4 _aInternet of Things.
650 2 4 _aBig Data.
650 2 4 _aMachine Learning.
650 2 4 _aComputational Intelligence.
650 2 4 _aWireless and Mobile Communication.
700 1 _aSarveshwaran, Velliangiri.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
700 1 _aChen, Joy Iong-Zong.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
700 1 _aPelusi, Danilo.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
710 2 _aSpringerLink (Online service)
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9789819921140
776 0 8 _iPrinted edition:
_z9789819921164
776 0 8 _iPrinted edition:
_z9789819921171
830 0 _aAdvanced Technologies and Societal Change,
_x2191-6861
856 4 0 _uhttps://doi.org/10.1007/978-981-99-2115-7
912 _aZDB-2-SCS
912 _aZDB-2-SXCS
942 _cSPRINGER
999 _c178822
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