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008			230613s2023 si | o |||| 0|eng d
020			_a9789819921157
024	7		_a10.1007/978-981-99-2115-7 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aQ334 _b2023 EB
245	0	0	_aArtificial Intelligence and Cyber Security in Industry 40 _cedited by Velliangiri Sarveshwaran, Joy Iong-Zong Chen, Danilo Pelusi
250			_a1st ed 2023
264		1	_aSingapore _bSpringer Nature _c2023
300			_a1 recurso en línea
336			_atexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
347			_atext file _bPDF _2rda
490	0		_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.
988			_aSpringer_Computer_2023
650		7	_2embne _9413115 _aInteligencia artificial
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-981-99-2115-7 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b02/2024 _dz _ek _zSI