Machine Learning and Data Mining for Computer Security: Methods and Applications / Edition 1

Machine Learning and Data Mining for Computer Security: Methods and Applications / Edition 1

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by Marcus A. Maloof
     
 

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ISBN-10: 1849965447

ISBN-13: 9781849965446

Pub. Date: 12/16/2011

Publisher: 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. 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,

Overview

"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.

Product Details

ISBN-13:
9781849965446
Publisher:
Springer London
Publication date:
12/16/2011
Series:
Advanced Information and Knowledge Processing Series
Edition description:
Softcover reprint of hardcover 1st ed. 2006
Pages:
210
Product dimensions:
6.10(w) x 9.25(h) x 0.02(d)

Table of Contents

Survey Contributions.- An Introduction to Information Assurance.- Some Basic Concept of Machine Learning and Data Mining.- Research Contributions.- Learning to Detect Malicious Executables.- Data Mining Applied to Intrusion Detection: MITRE Experiences.- Intrusion Detection Alarm Clustering.- Behavioral Features for Network Anomaly Detection.- Cost-Sensitive Modeling for Intrusion Detection.- Data Cleaning and Enriched Representations for Anomaly Detection in System Calls.- A Decision-Theoritic, Semi-Supervised Model for Intrusion Detection.

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