Dhruba Kumar Bhattacharyya, Jugal Kumar Kalita

Network Anomaly Detection (eBook, PDF)

A Machine Learning Perspective

• Format: PDF

Produktbeschreibung

With the rapid rise in the ubiquity and sophistication of Internet technology and the accompanying growth in the number of network attacks, network intrusion detection has become increasingly important. Anomaly-based network intrusion detection refers to finding exceptional or nonconforming patterns in network traffic data compared to normal behavi

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Produktdetails

• Verlag: Taylor & Francis
• Seitenzahl: 366
• Erscheinungstermin: 18. Juni 2013
• Englisch
• ISBN-13: 9781466582095
• Artikelnr.: 40068544

Autorenporträt

Dhruba Kumar Bhattacharyya is a professor in computer science and engineering at Tezpur University. Professor Bhattacharyya's research areas include network security, data mining, and bioinformatics. He has published more than 180 research articles in leading international journals and peer-reviewed conference proceedings. Dr. Bhattacharyya has written or edited seven technical books in English and two technical reference books in Assamese. He is on the editorial board of several international journals and has also been associated with several international conferences. For more about Dr. Bhattacharyya, see his profile at Tezpur University.

Jugal Kumar Kalita teaches computer science at the University of Colorado, Colorado Springs. His expertise is in the areas of artificial intelligence and machine learning, and the application of techniques in machine learning to network security, natural language processing, and bioinformatics. He has published 115 papers in journals and refereed conferences, and is the author of a book on Perl. He received the Chancellor's Award at the University of Colorado in 2011, in recognition of lifelong excellence in teaching, research, and service. For more about Dr. Kalita, see his profile at the University of Colorado.

Inhaltsangabe

Introduction. Networks and Anomalies. An Overview of Machine Learning
Methods. Detecting Anomalies in Network Data. Feature Selection. Approaches
to Network Anomaly Detection. Evaluation Methods. Tools and Systems.
Discussion. Open Issues, Challenges and Concluding Remarks. References.
Index.