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ANN: New Book: Machine learning and data mining for computer security

 
 
Mark Maloof
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      02-11-2006
Machine Learning and Data Mining for Computer Security
Methods and Applications
Series: Advanced Information and Knowledge Processing
2006, XVI, 210 p. 23 illus., Hardcover
ISBN: 1-84628-029-X
Springer, London

"Machine Learning and Data Mining for Computer Security" provides an
overview of the current state of research in machine learning and data
mining as it applies to problems in computer security. The first part
surveys the data sources, the learning and mining methods, evaluation
methodologies, and past work relevant for computer security. The
second part consists of articles written by the top researchers working
in this area. These articles deal with topics of host-based intrusion
detection through the analysis of audit trails, of command sequences
and of system calls as well as network intrusion detection through the
analysis of TCP packets and the detection of malicious executables.

Contents

* Foreword, Dorothy Denning

* An Introduction to Information Assurance
Clay Shields

* Some Basic Concepts of Machine Learning and Data Mining
Marcus A. Maloof

* Learning to Detect Malicious Executables
Jeremy Z. Kolter, Marcus A. Maloof

* Data Mining Applied to Intrusion Detection: MITRE Experiences
Eric E. Bloedorn, Lisa M. Talbot, David D. DeBarr

* Intrusion Detection Alarm Clustering
Klaus Julisch

* Behavioral Features for Network Anomaly Detection
James P. Early, Carla E. Brodley

* Cost-Sensitive Modeling for Intrusion Detection
Wenke Lee, Wei Fan, Salvatore J. Stolfo, Matthew Miller

* Data Cleaning and Enriched Representations for Anomaly Detection
in System Calls
Gaurav Tandon, Philip Chan, Debasis Mitra

* A Decision-Theoretic, Semi-Supervised Model for Intrusion Detection
Terran Lane

<http://www.springer.com/sgw/cda/frontpage/0,11855,5-102-22-52098821-0,00.html>

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