• Produktbild: Handbook of Big Data Privacy
  • Produktbild: Handbook of Big Data Privacy

Handbook of Big Data Privacy

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Beschreibung

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

19.03.2021

Herausgeber

Kim-Kwang Raymond Choo + weitere

Verlag

Springer

Seitenzahl

397

Maße (L/B/H)

23,5/15,5/2,3 cm

Gewicht

616 g

Auflage

1st ed. 2020

Sprache

Englisch

ISBN

978-3-030-38559-0

Beschreibung

Portrait

Kim-Kwang Raymond Choo holds the Cloud Technology Endowed Professorship at The University of Texas at San Antonio (UTSA), San Antonio, TX, USA. In 2015 he and his team won the Digital Forensics Research Challenge organized by Germany's University of Erlangen-Nuremberg. He is the recipient of the 2019 IEEE TCSC Award for Excellence in Scalable Computing (Middle Career Researcher), 2018 UTSA College of Business Col. Jean Piccione and Lt. Col. Philip Piccione Endowed Research Award for Tenured Faculty, British Computer Society's 2019 Wilkes Award Runner-up, 2019 EURASIP JWCN Best Paper Award, Korea Information Processing Society's JIPS Survey Paper Award (Gold) 2019, IEEE Blockchain 2019 Outstanding Paper Award, Best Paper Awards from IEEE TrustCom 2018 and ESORICS 2015, Fulbright Scholarship in 2009, 2008 Australia Day Achievement Medallion, and British Computer Society's Wilkes Award in 2008. He is also a Fellow of the Australian Computer Society, an IEEE Senior Member, and Co-Chair of IEEE Multimedia Communications Technical Committee's Digital Rights Management for Multimedia Interest Group.

Ali Dehghantanha is the director of Cyber Science Lab in the University of Guelph, Ontario, Canada. His lab is focused on building AI-powered solutions to support cyber threat attribution, cyber threat hunting and digital forensics tasks in Internet of Things (IoT), Industrial IoT, and Internet of Military of Things (IoMT) environments. Ali has served for more than a decade in a variety of industrial and academic positions with leading players in cyber security and AI. Prior to joining UofG, he has served as a Sr. Lecturer in the University of Sheffield - UK. He is an EU Marie-Curie Fellow alumnus and an IEEE Sr. member. He received his Ph.D. in Security in Computing in 2011 and his M.Sc. in Security in Computing in 2008.

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

19.03.2021

Herausgeber

Verlag

Springer

Seitenzahl

397

Maße (L/B/H)

23,5/15,5/2,3 cm

Gewicht

616 g

Auflage

1st ed. 2020

Sprache

Englisch

ISBN

978-3-030-38559-0

Herstelleradresse

Springer-Verlag GmbH
Tiergartenstr. 17
69121 Heidelberg
DE

Email: ProductSafety@springernature.com

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  • Produktbild: Handbook of Big Data Privacy
  • Produktbild: Handbook of Big Data Privacy
  • 1. 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.-