Big Data Analytics with Applications in Insider Threat Detection
1st Edition
Published on 1. December 2017
Book
Hardback
544 pages
978-1-4987-0547-9 (ISBN)
Description
Today's malware mutates randomly to avoid detection, but reactively adaptive malware is more intelligent, learning and adapting to new computer defenses on the fly. Using the same algorithms that antivirus software uses to detect viruses, reactively adaptive malware deploys those algorithms to outwit antivirus defenses and to go undetected. This book provides details of the tools, the types of malware the tools will detect, implementation of the tools in a cloud computing framework and the applications for insider threat detection.
More details
Language
English
Place of publication
Oxford
United States
Publishing group
Taylor & Francis Inc
Target group
College/higher education
Professional and scholarly
Illustrations
50 s/w Abbildungen
50 Illustrations, black and white
Dimensions
Height: 254 mm
Width: 178 mm
Weight
1200 gr
ISBN-13
978-1-4987-0547-9 (9781498705479)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions
Bhavani Thuraisingham | Pallabi Parveen | Mohammad Mehedy Masud
Big Data Analytics with Applications in Insider Threat Detection
Book
09/2020
1st Edition
CRC Press
€59.51
Shipment within 15-20 days
Bhavani Thuraisingham | Pallabi Parveen | Mohammad Mehedy Masud
Big Data Analytics with Applications in Insider Threat Detection
E-Book
11/2017
1st Edition
Auerbach Publishers Inc.
€63.49
Available for download
Bhavani Thuraisingham | Pallabi Parveen | Mohammad Mehedy Masud
Big Data Analytics with Applications in Insider Threat Detection
E-Book
11/2017
Auerbach
€63.49
Available for download
Persons
Dr. Bhavani Thuraisingham is the Louis A. Beecherl, Jr. Distinguished Professor of Computer Science and the Executive Director of the Cyber Security Research and Education Institute (CSI) at the University of Texas at Dallas.
Dr. Kevin W. Hamlen is an Assistant Professor in CS at UTD where he directs the Software Security Lab.
Dr. Latifur R. Khan is currently an Associate Professor in CS at UTD.
Dr. Mehedy Masud is an associate professor at the College of Information Technology, United Arab Emirates University.
Dr. Kevin W. Hamlen is an Assistant Professor in CS at UTD where he directs the Software Security Lab.
Dr. Latifur R. Khan is currently an Associate Professor in CS at UTD.
Dr. Mehedy Masud is an associate professor at the College of Information Technology, United Arab Emirates University.
Content
Supporting Technologies. Introduction. Data Mining Techniques. Cyber Security and Malware. Data Mining for Malware Detection. Conclusion. Stream-Based Novel Class Detection. Stream Mining. Novel Class Detection Problem. SNOD. Conclusion. Reactively Adaptive Malware. Reactively Adaptive Malware. RAMAL Design. RAMAL Implementation. SNODMAL. Introduction. SNODMAL Design. SNODMAL Implementation. SNODMAL FOR RAMAL. SNODMAL Extensions. Introduction. SNODMAL on the Cloud. SNODCAL. SNODMAL++. Conclusion. Summary and Directions. References. Appendix A: Data Management Systems. Appendix B: Malware Products.