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 Machine Learning & Data Mining for Computer Security: Methods & Applications
  

  Machine Learning & Data Mining for Computer Security: Methods & Applications by Edited by Marcus A. Maloof

  • Published by: SPRINGER
  • Author: Edited by Marcus A. Maloof
  • Page Count: 210
  • Group: SECURITY & ENCRYPTION
  • ISBN: 184628029X / 9781846280290
  • Published: Oct 2005

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Book Information and Description:

Machine Learning & Data Mining for Computer Security: Methods & Applications
This work talks about machine learning and data mining for computer security.


Contents:

http://www.springer.com/1-84628-029-X
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
XVI Contents
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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