dc.contributor.author |
Ganapathi Padmavathi |
|
dc.contributor.author |
Shanmugapriya D., |
|
dc.date.accessioned |
2024-01-29T22:13:22Z |
|
dc.date.available |
2024-01-29T22:13:22Z |
|
dc.date.issued |
2020 |
|
dc.identifier.citation |
Handbook of research on machine and deep learning applications for cyber security - 1 online resource (482 pages) - URL: https://libweb.kpfu.ru/ebsco/pdf/2227914.pdf |
|
dc.identifier.isbn |
1522596135 |
|
dc.identifier.isbn |
9781522596141 |
|
dc.identifier.isbn |
1522596143 |
|
dc.identifier.isbn |
9781522596134 |
|
dc.identifier.uri |
https://dspace.kpfu.ru/xmlui/handle/net/180415 |
|
dc.description |
Includes bibliographical references and index. |
|
dc.description.abstract |
"This book explores the use of machine learning and deep learning applications in the areas of cyber security and cyber-attack handling mechanisms"-- |
|
dc.description.tableofcontents |
Chapter 1. Review on intelligent algorithms for cyber security -- Chapter 2. A review on cyber security mechanisms using machine and deep learning algorithms -- Chapter 3. Review on machine and deep learning applications for cyber security -- Chapter 4. Applications of machine learning in cyber security domain -- Chapter 5. Applications of machine learning in cyber security -- Chapter 6. Malware and anomaly detection using machine learning and deep learning methods -- Chapter 7. Cyber threats detection and mitigation using machine learning -- Chapter 8. Hybridization of machine learning algorithm in intrusion detection system -- Chapter 9. A hybrid approach to detect the malicious applications in android-based smartphones using deep learning -- Chapter 10. Anomaly-based intrusion detection: adapting to present and forthcoming communication environments -- Chapter 11. Traffic analysis of UAV networks using enhanced deep feed forward neural networks (EDFFNN) -- Chapter 12. A novel biometric image enhancement approach with the hybridization of undecimated wavelet transform and deep autoencoder -- Chapter 13. A 3D-cellular automata-based publicly-verifiable threshold secret sharing -- Chapter 14. Big data analytics for intrusion detection: an overview -- Chapter 15. Big data analytics with machine learning and deep learning methods for detection of anomalies in network traffic -- Chapter 16. A secure protocol for high-dimensional big data providing data privacy -- Chapter 17. A review of machine learning methods applied for handling zero-day attacks in the cloud environment -- Chapter 18. Adoption of machine learning with adaptive approach for securing CPS -- Chapter 19. Variable selection method for regression models using computational intelligence techniques. |
|
dc.language |
English |
|
dc.language.iso |
en |
|
dc.subject.other |
Computer networks -- Security measures. |
|
dc.subject.other |
Computer security -- Data processing. |
|
dc.subject.other |
Computer crimes -- Prevention -- Data processing. |
|
dc.subject.other |
Machine learning. |
|
dc.subject.other |
Electronic books. |
|
dc.title |
Handbook of research on machine and deep learning applications for cyber security/ Padmavathi Ganapathi and D. Shanmugapriya, editors. |
|
dc.type |
Book |
|
dc.contributor.org |
IGI Global, |
|
dc.description.pages |
1 online resource (482 pages) |
|
dc.collection |
Электронно-библиотечные системы |
|
dc.source.id |
EN05CEBSCO05C1456 |
|