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Machine Learning for Computer and Cyber Security
Principle, Algorithms, and Practices
Series: Cyber Ecosystem and Security
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Machine Learning for Computer and Cyber Security
$41200
Unit price
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Unavailable
Book Hero Magic created this recommendation. While it's new and still learning, it may not be perfect - your feedback is welcome! IS THIS YOUR NEXT READ?
This book is ideal for IT professionals, cybersecurity experts, researchers, graduate students, and software developers specialising in computer science and engineering fields interested in machine learning applications for security purposes. It serves as both an educational and reference tool for those aiming to deepen their knowledge and develop innovative solutions against cyber threats.
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See moreThis comprehensive book offers valuable insights while using a wealth of examples and illustrations to effectively demonstrate the principles, algorithms, challenges and applications of machine learning and data mining for computer and cyber security.
Book Hero Magic formatted this description to make it easier to read. While it's new and
still learning, it may not be perfect - your
feedback is welcome!
Description
While Computer Security is a broader term that incorporates technologies, protocols, standards, and policies to ensure the security of computing systems—including computer hardware, software, and the information stored within it—Cyber Security is a specific, growing field focused on protecting computer networks (both offline and online) from unauthorized access, botnets, phishing scams, etc. Machine learning, a branch of Computer Science, enables computing machines to adopt new behaviours based on observable and verifiable data and information. It can be applied to ensure the security of computers and information by detecting anomalies using data mining and other techniques.
Machine Learning for Computer and Cyber Security is an invaluable resource for understanding the importance of machine learning and data mining in establishing computer and cyber security. It emphasises important security aspects associated with computer and cyber security, along with the analysis of machine learning and data mining-based solutions. The book also highlights future research domains where these solutions can be applied. Furthermore, it caters to the needs of IT professionals, researchers, faculty members, scientists, graduate students, research scholars, and software developers seeking to conduct research and develop combating solutions in cyber security using machine learning approaches. It is an extensive source of information for readers in the field of Computer Science and Engineering, as well as Cyber Security professionals.
Key Features:
- This book contains examples and illustrations to demonstrate the principles, algorithms, challenges, and applications of machine learning and data mining for computer and cyber security.
- It showcases important security aspects and current trends in the field.
- It provides insight into future research directions in the field.
- The content helps students prepare to exercise better defence by understanding attackers' motivations and how to deal with and mitigate situations using machine learning-based approaches.
Series: Cyber Ecosystem and Security
View all-
Machine Learning for Computer and Cyber Security
While Computer Security is a broader term that incorporates technologies, protocols, standards, and policies to ensure the security of computing systems—including computer hardware, software, and the information stored within it—Cyber Security is a specific, growing field focused on protecting computer networks (both offline and online) from unauthorized access, botnets, phishing scams, etc. Machine learning, a branch of Computer Science, enables...Hardback$41200Unit price /UnavailableAvailableShips in 4-6 weeks
Book Details
INFORMATION
ISBN: 9781138587304
Publisher: Taylor & Francis Ltd
Format: Hardback
Date Published: 13 February 2019
Country: United Kingdom
Imprint: CRC Press
Illustration: 49 Tables, black and white; 8 Illustrations, color; 126 Illustrations, black and white
Contributors:
- Edited by Brij B. Gupta
- Edited by Quan Z. Sheng
Audience: Professional and scholarly
DIMENSIONS
Width: 156.0mm
Height: 234.0mm
Weight: 740g
Pages: 352
About the Author
Brij B. Gupta received PhD degree from Indian Institute of Technology Roorkee, India in Information and Cyber Security. He published more than 175 research papers in International Journals and Conferences of high repute including IEEE, Elsevier, ACM, Springer, Wiley, Taylor & Francis, Inderscience, etc. He has visited several countries, i.e. Canada, Japan, Malaysia, Australia, China, Hong-Kong, Italy, Spain etc to present his research work. His biography was selected and published in the 30th Edition of Marquis Who's Who in the World, 2012. Dr. Gupta also received Young Faculty research fellowship award from Ministry of Electronics and Information Technology, Government of India in 2017. He is also working as principal investigator of various R&D projects. He is serving as associate editor of IEEE Access, IEEE TII, and Executive editor of IJITCA, Inderscience, respectively. At present, Dr. Gupta is working as Assistant Professor in the Department of Computer Engineering, National Institute of Technology Kurukshetra India. His research interest includes Information security, Cyber Security, Mobile security, Cloud Computing, Web security, Intrusion detection and Phishing.
Michael Sheng is a full Professor and Head of Department of Computing at Macquarie University. Before moving to Macquarie, Michael spent 10 years at School of Computer Science, the University of Adelaide (UoA). Michael holds a PhD degree in computer science from the University of New South Wales (UNSW) and did his post-doc as a research scientist at CSIRO ICT Centre. From 1999 to 2001, Sheng also worked at UNSW as a visiting research fellow. Prior to that, he spent 6 years as a senior software engineer in industries.
Prof. Sheng has more than 280 publications as edited books and proceedings, refereed book chapters, and refereed technical papers in journals and conferences including ACM Computing Surveys, ACM TOIT, ACM TOMM, ACM TKDD, VLDB Journal, Computer (Oxford), IEEE TPDS, TKDE, DAPD, IEEE TSC, WWWJ, IEEE Computer, IEEE Internet Computing, Communications of the ACM, VLDB, ICDE, ICDM, CIKM, EDBT, WWW, ICSE, ICSOC, ICWS, and CAiSE. Dr. Michael Sheng is the recipient of the ARC Future Fellowship (2014), Chris Wallace Award for Outstanding Research Contribution (2012), and Microsoft Research Fellowship (2003). He is a member of the IEEE and the ACM. Homepage: https://web.science.mq.edu.au/~qsheng/
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