Information Fusion for Cyber-Security Analytics / Studies in Computational Intelligence Bd.691 (PDF)
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This book highlights several gaps that have not been addressed in existing cyber security research. It first discusses the recent attack prediction techniques that utilize one or more aspects of information to create attack prediction models. The second part is dedicated to new trends on information fusion and their applicability to cyber security; in particular, graph data analytics for cyber security, unwanted traffic detection and control based on trust management software defined networks, security in wireless sensor networks & their applications, and emerging trends in security system design using the concept of social behavioral biometric. The book guides the design of new commercialized tools that can be introduced to improve the accuracy of existing attack prediction models. Furthermore, the book advances the use of Knowledge-based Intrusion Detection Systems (IDS) to complement existing IDS technologies. It is aimed towards cyber security researchers.
Dr. George Karabatis is an Associate Professor of Information Systems and Associate Chair for Academic Affairs in the Department of Information Systems. He teaches undergraduate and graduate courses in semantic data integration, data management, data communications and networking, database applications, and mobile applications. He is one of the founding members of DINAMIC, a research group of IS faculty and students who pursue research in intelligent information discovery in various application domains. His research work has been published in peer-reviewed journals, conference proceedings and book chapters. He has been funded by NSF, USGS, MD Board of Elections, Northrop-Grumman and IGSR. He holds a Ph.D. in Computer Science from the University of Houston.
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- 2016, 1st ed. 2017, 379 Seiten, Englisch
- Herausgegeben: Izzat M Alsmadi, George Karabatis, Ahmed Aleroud
- Verlag: Springer-Verlag GmbH
- ISBN-10: 3319442570
- ISBN-13: 9783319442570
- Erscheinungsdatum: 21.10.2016
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- Grösse: 7.69 MB
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