Applications of Data Mining in Computer Security

ISBN-13: 9781402070549 / Angielski / Twarda / 2002 / 252 str.

Daniel Barbara; Sushil Jajodia; Daniel Barbara

Opis

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. Data mining has been loosely defined as the process of extracting information from large amounts of data. In the context of security, the information we are seeking is the knowledge of whether a security breach has been experienced, and if the answer is yes, who is the perpetrator. This information could be collected in the context of discovering intrusions that aim to breach the privacy of services, data in a computer system or alternatively, in the context of discovering evidence left in a computer system as part of criminal activity. 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. Applications Of Data Mining In Computer Security is designed to meet the needs of a professional audience composed of researchers and practitioners in industry and graduate level students in computer science.

Spis treści

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.

O autorze

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