Trust, Security and Privacy for Big Data (eBook, ePUB)
Redaktion: Alazab, Mamoun; Gupta, Maanak
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Trust, Security and Privacy for Big Data (eBook, ePUB)
Redaktion: Alazab, Mamoun; Gupta, Maanak
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The book provides timely and comprehensive information for researchers and industry partners in communications and networking domains to review the latest results in security and privacy related work of big data.
- Geräte: eReader
- ohne Kopierschutz
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- Größe: 20.5MB
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The book provides timely and comprehensive information for researchers and industry partners in communications and networking domains to review the latest results in security and privacy related work of big data.
Dieser Download kann aus rechtlichen Gründen nur mit Rechnungsadresse in A, B, BG, CY, CZ, D, DK, EW, E, FIN, F, GR, HR, H, IRL, I, LT, L, LR, M, NL, PL, P, R, S, SLO, SK ausgeliefert werden.
Produktdetails
- Produktdetails
- Verlag: Taylor & Francis
- Seitenzahl: 212
- Erscheinungstermin: 30. Juni 2022
- Englisch
- ISBN-13: 9781000619133
- Artikelnr.: 63794148
- Verlag: Taylor & Francis
- Seitenzahl: 212
- Erscheinungstermin: 30. Juni 2022
- Englisch
- ISBN-13: 9781000619133
- Artikelnr.: 63794148
- Herstellerkennzeichnung Die Herstellerinformationen sind derzeit nicht verfügbar.
Mamoun Alazab is an associate professor at the College of Engineering, IT and Environment at Charles Darwin University, Australia. He received his Ph.D. degree in Computer Science from the Federation University of Australia, School of Science, Information Technology and Engineering. He is a cybersecurity researcher and practitioner with industry and academic experience. Alazab's research is multidisciplinary that focuses on cybersecurity and digital forensics of computer systems with a focus on cybercrime detection and prevention. He has more than 150 research papers in many international journals and conferences. He works closely with government and industry on many projects, including Northern Territory (NT) Department of Information and Corporate Services, IBM, Trend Micro, the Australian Federal Police (AFP), the Australian Communications and Media Authority (ACMA), Westpac, United Nations Office on Drugs and Crime (UNODC), and the Attorney General's Department. He is a senior member of the IEEE. He is the founding chair of the IEEE Northern Territory (NT) Subsection. Maanak Gupta is an assistant professor in Department of Computer Science at Tennessee Tech University, USA. He received his Ph.D. in Computer Science from the University of Texas at San Antonio and has worked as a postdoctoral research fellow at the Institute for Cyber Security. He also holds an M.S. degree in Information Systems from Northeastern University, Boston. His primary area of research includes security and privacy in cyber space focused in studying foundational aspects of access control and their application in technologies including cyber physical systems, cloud computing, IoT and Big data. Dr Gupta has worked in developing novel security mechanisms, models and architectures for next generation smart cars, smart cities, intelligent transportation systems and smart farming. He is also interested in machine learning based malware analysis and AI assisted cyber security solutions. His scholarly work is regularly published at top peer-reviewed security venues including ACM SIGSAC conferences and refereed journals. His research has been funded by the US National Science Foundation (NSF), NASA, US Department of Defense (DoD) and private industry. He is also the co-founder and co-chair of SMARTFARM-2020 and SaT-CPS 2021 workshops. In addition, he is a reviewer for several journals and conferences.
1. DigImoPriv: A Big Data Framework for Preserving Privacy of Digital
Immortals 2. Federated Learning Role in Big Data, Iot Services and
Applications Security, Privacy and Trust in Iot: A Survey 3. From the Cloud
to the Edge: Towards a Distributed and Light Weight Secure Big Data
Pipelines for IoT Applications 4. Ground Point Filtering and Digital
Terrain Model Generation using LiDAR Data 5. Predictive Big Data Analytics
and Privacy based Decision Support System 6. Fingerprinting Based
Positioning Techniques Using Machine Learning Algorithms: Principles,
Approaches and Challenges 7. Recent Advancements in Network and Cyber
Security using RNN 8. A Big Data Framework for Dynamic Consent 9. A
Low-Level Hybrid Intrusion Detection System Based on Hardware Performance
Counters 10. Comparative Study on Machine Learning Methods to Detect
Metamorphic Threats
Immortals 2. Federated Learning Role in Big Data, Iot Services and
Applications Security, Privacy and Trust in Iot: A Survey 3. From the Cloud
to the Edge: Towards a Distributed and Light Weight Secure Big Data
Pipelines for IoT Applications 4. Ground Point Filtering and Digital
Terrain Model Generation using LiDAR Data 5. Predictive Big Data Analytics
and Privacy based Decision Support System 6. Fingerprinting Based
Positioning Techniques Using Machine Learning Algorithms: Principles,
Approaches and Challenges 7. Recent Advancements in Network and Cyber
Security using RNN 8. A Big Data Framework for Dynamic Consent 9. A
Low-Level Hybrid Intrusion Detection System Based on Hardware Performance
Counters 10. Comparative Study on Machine Learning Methods to Detect
Metamorphic Threats
1. DigImoPriv: A Big Data Framework for Preserving Privacy of Digital
Immortals 2. Federated Learning Role in Big Data, Iot Services and
Applications Security, Privacy and Trust in Iot: A Survey 3. From the Cloud
to the Edge: Towards a Distributed and Light Weight Secure Big Data
Pipelines for IoT Applications 4. Ground Point Filtering and Digital
Terrain Model Generation using LiDAR Data 5. Predictive Big Data Analytics
and Privacy based Decision Support System 6. Fingerprinting Based
Positioning Techniques Using Machine Learning Algorithms: Principles,
Approaches and Challenges 7. Recent Advancements in Network and Cyber
Security using RNN 8. A Big Data Framework for Dynamic Consent 9. A
Low-Level Hybrid Intrusion Detection System Based on Hardware Performance
Counters 10. Comparative Study on Machine Learning Methods to Detect
Metamorphic Threats
Immortals 2. Federated Learning Role in Big Data, Iot Services and
Applications Security, Privacy and Trust in Iot: A Survey 3. From the Cloud
to the Edge: Towards a Distributed and Light Weight Secure Big Data
Pipelines for IoT Applications 4. Ground Point Filtering and Digital
Terrain Model Generation using LiDAR Data 5. Predictive Big Data Analytics
and Privacy based Decision Support System 6. Fingerprinting Based
Positioning Techniques Using Machine Learning Algorithms: Principles,
Approaches and Challenges 7. Recent Advancements in Network and Cyber
Security using RNN 8. A Big Data Framework for Dynamic Consent 9. A
Low-Level Hybrid Intrusion Detection System Based on Hardware Performance
Counters 10. Comparative Study on Machine Learning Methods to Detect
Metamorphic Threats