Applications of Data Mining in Computer Security

by Daniel Barbará (Editor), Sushil Jajodia (Editor)

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:	9781402070549
Publisher:	Springer US
Publication date:	05/31/2002
Series:	Advances in Information Security , #6
Edition description:	2002
Pages:	252
Product dimensions:	6.10(w) x 9.25(h) x 0.36(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.

Editorial Reviews

It is estimated that less that four percent of computer intrusions are detected or reported, but computer intrusions usually leave traces in audit data trails. This volume presents nine papers that explore the use of data mining for the creation of tools for the detection and analysis of intrusions and attacks. Topics include an architecture for anomaly detection, a geometric framework for unsupervised anomaly detection, fusing a heterogeneous alert stream into scenarios, adaptive model generation, and e-mail authorship attribution for computer forensics. Annotation c. Book News, Inc., Portland, OR