Sumeet Dua, Xian Du

Data Mining and Machine Learning in Cybersecurity (eBook, PDF)

• Format: PDF

Produktbeschreibung

From basic concepts in machine learning and data mining to advanced problems in the machine learning domain, this book provides a unified reference for specific machine learning solutions to cybersecurity problems. It supplies a foundation in cybersecurity fundamentals, surveys contemporary challenges, and details cutting-edge machine learning and data mining techniques. This interdisciplinary resource includes technique review tables that allow for speedy access to common cybersecurity problems and associated data mining methods. Numerous illustrative figures and more than 40 case studies help readers visualize the workflow of complex techniques.

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Produktdetails

• Verlag: Taylor & Francis
• Seitenzahl: 256
• Erscheinungstermin: 19. April 2016
• Englisch
• ISBN-13: 9781439839430
• Artikelnr.: 38442917

Autorenporträt

Dr. Sumeet Dua is currently an upchurch endowed associate professor and the coordinator of IT research at Louisiana Tech University, Ruston, USA. He received his PhD in computer science from Louisiana State University, Baton Rouge, Louisiana.

His areas of expertise include data mining, image processing and computational decision support, pattern recognition, data warehousing, biomedical informatics, and heterogeneous distributed data integration. The National Science Foundation (NSF), the National Institutes of Health (NIH), the Air Force Research Laboratory (AFRL), the Air Force Office of Sponsored Research (AFOSR), the National Aeronautics and Space Administration (NASA), and the Louisiana Board of Regents (LA-BoR) have funded his research with over $2.8 million. He frequently serves as a study section member (expert panelist) for the National Institutes of Health (NIH) and panelist for the National Science Foundation (NSF)/CISE Directorate. Dr. Dua has chaired several conference sessions in the area of data mining and is the program chair for the Fifth International Conference on Information Systems, Technology, and Management (ICISTM-2011). He has given more than 26 invited talks on data mining and its applications at international academic and industry arenas, has advised more than 25 graduate theses, and currently advises several graduate students in the discipline. Dr. Dua is a coinventor of two issued U.S. patents, has (co-)authored more than 50 publications and book chapters, and has authored or edited four books. Dr. Dua has received the Engineering and Science Foundation Award for Faculty Excellence (2006) and the Faculty Research Recognition Award (2007), has been recognized as a distinguished researcher (2004-2010) by the Louisiana Biomedical Research Network (NIH-sponsored), and has won the Outstanding Poster Award at the NIH/NCI caBIG-NCRI Informatics Joint Conference; Biomedical Informatics without Border

Inhaltsangabe

Introduction. Classical Machine-Learning Paradigms for Data Mining.
Supervised Learning for Misuse/Signature Detection. Machine Learning for
Anomaly Detection. Machine Learning for Hybrid Detection. Machine Learning
for Scan Detection. Machine Learning for Profiling Network Traffic.
Privacy-Preserving Data Mining. Emerging Challenges in Cybersecurity.
Index.