Network Anomaly Detection

A Machine Learning Perspective
1. Auflage 2013
ISBN: 978-1-4665-8208-8
Verlag: Chapman and Hall/CRC

Buch, Englisch, 366 Seiten, Format (B × H): 161 mm x 240 mm, Gewicht: 716 g

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 behavior. Finding these anomalies has extensive applications in areas such as cyber security, credit card and insurance fraud detection, and military surveillance for enemy activities. Network Anomaly Detection: A Machine Learning Perspective presents machine learning techniques in depth to help you more effectively detect and counter network intrusion.

In this book, you’ll learn about:

- Network anomalies and vulnerabilities at various layers

- The pros and cons of various machine learning techniques and algorithms

- A taxonomy of attacks based on their characteristics and behavior

- Feature selection algorithms

- How to assess the accuracy, performance, completeness, timeliness, stability, interoperability, reliability, and other dynamic aspects of a network anomaly detection system

- Practical tools for launching attacks, capturing packet or flow traffic, extracting features, detecting attacks, and evaluating detection performance

- Important unresolved issues and research challenges that need to be overcome to provide better protection for networks

Examining numerous attacks in detail, the authors look at the tools that intruders use and show how to use this knowledge to protect networks. The book also provides material for hands-on development, so that you can code on a testbed to implement detection methods toward the development of your own intrusion detection system. It offers a thorough introduction to the state of the art in network anomaly detection using machine learning approaches and systems.

Zielgruppe

Undergraduate and graduate students studying network and computer security in computer science, computer engineering, and information technology programs.

Autoren/Hrsg.

Bhattacharyya, Dhruba Kumar

Kalita, Jugal Kumar

Fachgebiete

• Mathematik | Informatik EDV | Informatik Computerkommunikation & -vernetzung Netzwerksicherheit
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Maschinelles Lernen

Inhaltsverzeichnis

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.

Über Autor(innen)

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.