Artificial Intelligence and Cyber Security in Industry 4.0
This 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 l...
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| 団体著者: | |
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| その他の著者: | , , |
| フォーマット: | 電子媒体 eBook |
| 言語: | 英語 |
| 出版事項: |
Singapore :
Springer Nature Singapore : Imprint: Springer,
2023.
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| 版: | 1st ed. 2023. |
| シリーズ: | Advanced Technologies and Societal Change,
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| 主題: | |
| オンライン･アクセス: | https://doi.org/10.1007/978-981-99-2115-7 |
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| 245 | 1 | 0 | |a Artificial Intelligence and Cyber Security in Industry 4.0 |h [electronic resource] / |c edited by Velliangiri Sarveshwaran, Joy Iong-Zong Chen, Danilo Pelusi. |
| 250 | |a 1st ed. 2023. | ||
| 264 | 1 | |a Singapore : |b Springer Nature Singapore : |b Imprint: Springer, |c 2023. | |
| 300 | |a 1 online resource (VIII, 373 p. 100 illus.). |b 86 illus. in color. | ||
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| 490 | 1 | |a Advanced Technologies and Societal Change, |x 2191-6861 | |
| 505 | 0 | |a Introduction 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 | |a This 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. | ||
| 532 | 8 | |a Accessibility summary: This PDF does not fully comply with PDF/UA standards, but does feature limited screen reader support, described non-text content (images, graphs), bookmarks for easy navigation and searchable, selectable text. Users of assistive technologies may experience difficulty navigating or interpreting content in this document. We recognize the importance of accessibility, and we welcome queries about accessibility for any of our products. If you have a question or an access need, please get in touch with us at accessibilitysupport@springernature.com. | |
| 532 | 8 | |a No reading system accessibility options actively disabled | |
| 532 | 8 | |a Publisher contact for further accessibility information: accessibilitysupport@springernature.com | |
| 588 | |a Print version record. | ||
| 650 | 0 | |a Artificial intelligence. | |
| 650 | 0 | |a Internet of things. | |
| 650 | 0 | |a Big data. | |
| 650 | 0 | |a Machine learning. | |
| 650 | 0 | |a Computational intelligence. | |
| 650 | 0 | |a Wireless communication systems. | |
| 650 | 0 | |a Mobile communication systems. | |
| 650 | 1 | 4 | |a Artificial Intelligence. |
| 650 | 2 | 4 | |a Internet of Things. |
| 650 | 2 | 4 | |a Big Data. |
| 650 | 2 | 4 | |a Machine Learning. |
| 650 | 2 | 4 | |a Computational Intelligence. |
| 650 | 2 | 4 | |a Wireless and Mobile Communication. |
| 700 | 1 | |a Sarveshwaran, Velliangiri. |e editor. |4 edt |4 http://id.loc.gov/vocabulary/relators/edt | |
| 700 | 1 | |a Chen, Joy Iong-Zong. |e editor. |0 (orcid)0000-0001-6150-3662 |1 https://orcid.org/0000-0001-6150-3662 |4 edt |4 http://id.loc.gov/vocabulary/relators/edt | |
| 700 | 1 | |a Pelusi, Danilo. |e editor. |4 edt |4 http://id.loc.gov/vocabulary/relators/edt | |
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