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020 _a9783030385576
_9978-3-030-38557-6
024 7 _a10.1007/978-3-030-38557-6
_2doi
050 4 _aQA76.9.A25
072 7 _aUR
_2bicssc
072 7 _aUTN
_2bicssc
072 7 _aCOM053000
_2bisacsh
072 7 _aUR
_2thema
072 7 _aUTN
_2thema
082 0 4 _a005.8
_223
245 1 0 _aHandbook of Big Data Privacy
_h[electronic resource] /
_cedited by Kim-Kwang Raymond Choo, Ali Dehghantanha.
250 _a1st ed. 2020.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2020.
300 _aIX, 397 p. 149 illus., 141 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
505 0 _a1. Big Data and Privacy : Challenges and Opportunities -- 2. AI and Security of Critical Infrastructure -- 3. Industrial Big Data Analytics: Challenges and Opportunities -- 4. A Privacy Protection Key Agreement Protocol Based on ECC for Smart Grid -- 5. Applications of Big Data Analytics and Machine Learning in the Internet of Things -- 6. A Comparison of State-of-the-art Machine Learning Models for OpCode-Based IoT Malware Detection -- 7. Artificial Intelligence and Security of Industrial Control Systems -- 8. Enhancing Network Security via Machine Learning: Opportunities and Challenges -- 9. Network Security and Privacy Evaluation Scheme for Cyber Physical Systems (CPS) -- 10. Anomaly Detection in Cyber-Physical Systems Using Machine Learning -- 11. Big Data Application for Security of Renewable Energy Resources -- 12. Big-Data and Cyber-Physical Systems in Healthcare: Challenges and Opportunities -- 13. Privacy Preserving Abnormality Detection: A Deep Learning Approach.-14. Privacy and Security in Smart and Precision Farming: A Bibliometric Analysis -- 15. A Survey on Application of Big Data in Fin Tech Banking Security and Privacy -- 16. A Hybrid Deep Generative Local Metric Learning Method For Intrusion Detection -- 17. Malware elimination impact on dynamic analysis: An experimental machine learning approach -- 18. RAT Hunter: Building Robust Models for Detecting Remote Access Trojans Based on Optimum Hybrid Features -- 19. Active Spectral Botnet Detection based on Eigenvalue Weighting -- .
520 _aThis handbook provides comprehensive knowledge and includes an overview of the current state-of-the-art of Big Data Privacy, with chapters written by international world leaders from academia and industry working in this field. The first part of this book offers a review of security challenges in critical infrastructure and offers methods that utilize acritical intelligence (AI) techniques to overcome those issues. It then focuses on big data security and privacy issues in relation to developments in the Industry 4.0. Internet of Things (IoT) devices are becoming a major source of security and privacy concern in big data platforms. Multiple solutions that leverage machine learning for addressing security and privacy issues in IoT environments are also discussed this handbook. The second part of this handbook is focused on privacy and security issues in different layers of big data systems. It discusses about methods for evaluating security and privacy of big data systems on network, application and physical layers. This handbook elaborates on existing methods to use data analytic and AI techniques at different layers of big data platforms to identify privacy and security attacks. The final part of this handbook is focused on analyzing cyber threats applicable to the big data environments. It offers an in-depth review of attacks applicable to big data platforms in smart grids, smart farming, FinTech, and health sectors. Multiple solutions are presented to detect, prevent and analyze cyber-attacks and assess the impact of malicious payloads to those environments. This handbook provides information for security and privacy experts in most areas of big data including; FinTech, Industry 4.0, Internet of Things, Smart Grids, Smart Farming and more. Experts working in big data, privacy, security, forensics, malware analysis, machine learning and data analysts will find this handbook useful as a reference. Researchers and advanced-level computer science students focused on computer systems, Internet of Things, Smart Grid, Smart Farming, Industry 4.0 and network analysts will also find this handbook useful as a reference.
650 0 _aData protection.
650 0 _aComputer engineering.
650 0 _aComputer networks .
650 0 _aArtificial intelligence.
650 1 4 _aData and Information Security.
650 2 4 _aComputer Engineering and Networks.
650 2 4 _aArtificial Intelligence.
650 2 4 _aComputer Communication Networks.
700 1 _aChoo, Kim-Kwang Raymond.
_eeditor.
_0(orcid)0000-0001-9208-5336
_1https://orcid.org/0000-0001-9208-5336
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
700 1 _aDehghantanha, Ali.
_eeditor.
_0(orcid)0000-0002-9294-7554
_1https://orcid.org/0000-0002-9294-7554
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
710 2 _aSpringerLink (Online service)
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783030385569
776 0 8 _iPrinted edition:
_z9783030385583
776 0 8 _iPrinted edition:
_z9783030385590
856 4 0 _uhttps://doi.org/10.1007/978-3-030-38557-6
912 _aZDB-2-SCS
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