Applications of Data Mining in Computer Security

Applications of Data Mining in Computer Security

Paperback(Softcover reprint of the original 1st ed. 2002)

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Overview

Data mining is becoming a pervasive technology in activities as diverse as using historical data to predict the success of a marketing campaign, looking for patterns in financial transactions to discover illegal activities or analyzing genome sequences. From this perspective, it was just a matter of time for the discipline to reach the important area of computer security. Applications Of Data Mining In Computer Security presents a collection of research efforts on the use of data mining in computer security.

Applications Of Data Mining In Computer Security concentrates heavily on the use of data mining in the area of intrusion detection. The reason for this is twofold. First, the volume of data dealing with both network and host activity is so large that it makes it an ideal candidate for using data mining techniques. Second, intrusion detection is an extremely critical activity. This book also addresses the application of data mining to computer forensics. This is a crucial area that seeks to address the needs of law enforcement in analyzing the digital evidence.

Product Details

ISBN-13: 9781461353218
Publisher: Springer US
Publication date: 10/23/2012
Series: Advances in Information Security , #6
Edition description: Softcover reprint of the original 1st ed. 2002
Pages: 252
Product dimensions: 6.10(w) x 9.25(h) x 0.02(d)

About the Author

Dr. Sushil Jajodia is Professor and Chairman of the Dept. of Information and Software Engineering, and Director of the Center for Secure Information Systems at the George Mason University, Fairfax, Virginia, USA

Table of Contents

List of Figures. List of Tables.
Preface.
1. Modern Intrusion Detection, Data Mining, and Degrees of Attack Guilt; S. Noel, et al.
2. Data Mining for Intrusion Detection; K. Julisch.
3. An Architecture for Anomaly Detection; D. Barbará, et al.
4. A Geometric Framework for Unsupervised Anomaly Detection; E. Eskin, et al.
5. Fusing a Heterogeneous Alert Stream into Scenarios; O. Dain, K. Cunningham.
6. Using MIB II Variables for Network Intrusion Detection; Xinzhou Qin, et al.
7. Adaptive Model Generation; A. Honig, et al.
8. Proactive Intrusion Detection; J.B.D. Cabrera, et al.
9. References.
Index.

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