Machine Learning and Data Mining for Computer Security

Methods and Applications

herausgegeben von: Marcus A. Maloof, BS, MS, PhD

Verlag: Springer London

Buchreihe : Advanced Information and Knowledge Processing

Enthalten in: Springer Professional "Wirtschaft+Technik" , Springer Professional "Technik" , Springer Professional "Wirtschaft"

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Über dieses Buch

"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.

Inhaltsverzeichnis

Frontmatter

1. Introduction

Marcus A. Maloof

Survey Contributions

2. An Introduction to Information Assurance

Clay Shields

3. Some Basic Concept of Machine Learning and Data Mining

Marcus A. Maloof

Backmatter

Titel: Machine Learning and Data Mining for Computer Security
herausgegeben von: Marcus A. Maloof, BS, MS, PhD
Verlag: Springer London
Electronic ISBN: 978-1-84628-253-9
Print ISBN: 978-1-84628-029-0
DOI: https://doi.org/10.1007/1-84628-253-5

Springer Professional

Machine Learning and Data Mining for Computer Security

Methods and Applications

Über dieses Buch

Inhaltsverzeichnis

Frontmatter

1. Introduction

Survey Contributions

2. An Introduction to Information Assurance

3. Some Basic Concept of Machine Learning and Data Mining

Research Contributions

4. Learning to Detect Malicious Executables

5. Data Mining Applied to Intrusion Detection: MITRE Experiences

6. Intrusion Detection Alarm Clustering

7. Behavioral Features for Network Anomaly Detection

8. Cost-Sensitive Modeling for Intrusion Detection

9. Data Cleaning and Enriched Representations for Anomaly Detection in System Calls

10. A Decision-Theoritic, Semi-Supervised Model for Intrusion Detection

Backmatter