Information Fusion for Cyber-Security Analytics

Editors: Alsmadi, Izzat M, Karabatis, George, Aleroud, Ahmed (Eds.)
Helps readers understand how combining machine learning and reasoning techniques aids in creating attack prediction models with higher accuracy;
Provides information on utilizing several existing applications and tools to perform information fusion on machine learning tasks;
Presents readers with the tools to apply the learned knowledge daily cyber-security tasks

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.

Number of Pages
X, 379
Number of Illustrations and Tables
24 b/w illustrations, 61 illustrations in colour
Topics
Communications Engineering, Networks
Circuits and Systems
Systems and Data Security
Big Data/Analytics



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