Machine Learning and Data Mining for Computer Security

Methods and Applications
2006. Auflage 2005
ISBN: 978-1-84628-029-0
Verlag: Springer

Buch, Englisch, 210 Seiten, Format (B × H): 164 mm x 246 mm, Gewicht: 1100 g

Reihe: Advanced Information and Knowledge Processing

"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.

Zielgruppe

Researchers, practitioners, graduate students

Autoren/Hrsg.

Maloof, Marcus A

Fachgebiete

• Mathematik | Informatik EDV | Informatik Technische Informatik Computersicherheit
• Mathematik | Informatik EDV | Informatik Daten / Datenbanken Kryptologie, Informationssicherheit
• Mathematik | Informatik EDV | Informatik Daten / Datenbanken Data Mining
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Maschinelles Lernen

Inhaltsverzeichnis

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.