Overview

This book constitutes the refereed proceedings of the 6th International Symposium on Recent Advances in Intrusion Detection, RAID 2003, held in Pittsburgh, PA, USA in September 2003.The 13 revised full papers presented were carefully reviewed and selected from 44 submissions. The papers are organized in topical sections on network infrastructure, anomaly detection, modeling and specification, and IDS sensors.

Full Product Details

Author:   Giovanni Vigna ,  Erland Jonsson ,  Christopher Kruegel
Publisher:   Springer-Verlag Berlin and Heidelberg GmbH & Co. KG
Imprint:   Springer-Verlag Berlin and Heidelberg GmbH & Co. K
Edition:   2003 ed.
Volume:   2820
Dimensions:   Width: 15.50cm , Height: 1.30cm , Length: 23.50cm
Weight:   0.790kg
ISBN:  

9783540408789

ISBN 10:   3540408789
Pages:   242
Publication Date:   02 September 2003
Audience:   College/higher education ,  Professional and scholarly ,  Tertiary & Higher Education ,  Undergraduate
Format:   Paperback
Publisher's Status:   Active
Availability:   In Print  
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Table of Contents

Network Infrastructure.- Mitigating Distributed Denial of Service Attacks Using a Proportional-Integral-Derivative Controller.- Topology-Based Detection of Anomalous BGP Messages.- Anomaly Detection I.- Detecting Anomalous Network Traffic with Self-organizing Maps.- An Approach for Detecting Self-propagating Email Using Anomaly Detection.- Correlation.- Statistical Causality Analysis of INFOSEC Alert Data.- Correlation of Intrusion Symptoms: An Application of Chronicles.- Modeling and Specification.- Modeling Computer Attacks: An Ontology for Intrusion Detection.- Using Specification-Based Intrusion Detection for Automated Response.- IDS Sensors.- Characterizing the Performance of Network Intrusion Detection Sensors.- Using Decision Trees to Improve Signature-Based Intrusion Detection.- Ambiguity Resolution via Passive OS Fingerprinting.- Anomaly Detection II.- Two Sophisticated Techniques to Improve HMM-Based Intrusion Detection Systems.- An Analysis of the 1999 DARPA/Lincoln Laboratory Evaluation Data for Network Anomaly Detection.

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