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  SPECIFICATION
MACHINE LEARNING & DATA MINING FOR COMPUTER SECURITY METHODS
Author: MARCUS A. MALOOF
ISBN: 9788184898873
Year: 2006
Publisher: SPRINGER INDIA PRIVATE LIMITED
Category: ARTIFICIAL INTELLIGENCE
Edition: 1
Format: Paperback
Language: English
Pages: 210
 ABOUT THE TITLE
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. This book has a strong focus on information processing and combines and extends results from computer security. The first part of the book surveys the data sources, the learning and mining methods, evaluation methodologies, and past work relevant for computer security. The second part of the book consists of articles written by the top researchers working in this area. These articles deals 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. This book fills the great need for a book that collects and frames work on developing and applying methods from machine learning and data mining to problems in computer security.

Maloof, Marcus A. (Ed.)


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 TABLE OF CONTENTS
Contents
Foreword . . . . . . VII
Preface . . . . . . . . IX
1 Introduction
Marcus A. Maloof . . . . . . 1
Part I Survey Contributions
2 An Introduction to Information Assurance
Clay Shields . . . . . . . . . . . 7
3 Some Basic Concepts of Machine Learning and Data Mining
Marcus A. Maloof . . . . . . 23
Part II Research Contributions
4 Learning to Detect Malicious Executables
Jeremy Z. Kolter, Marcus A. Maloof . . . . . . 47
5 Data Mining Applied to Intrusion Detection: MITRE Experiences
Eric E. Bloedorn, Lisa M. Talbot, David D. DeBarr . . . . . . 65
6 Intrusion Detection Alarm Clustering
Klaus Julisch . . . . . . . . . . 89
7 Behavioral Features for Network Anomaly Detection
James P. Early, Carla E. Brodley . . . . . . . . 107
8 Cost-Sensitive Modeling for Intrusion Detection
Wenke Lee, Wei Fan, Salvatore J. Stolfo, Matthew Miller . . . . . . . 125
9 Data Cleaning and Enriched Representations for Anomaly Detection in System Calls
Gaurav Tandon, Philip Chan, Debasis Mitra . . . . . . . 137
10 A Decision-Theoretic, Semi-Supervised Model for Intrusion Detection
Terran Lane . . . . . . . . . . . 157
References . .. . . . . 179
Index . . . . . . . . 199
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