Understanding Intrusion Detection Through Visualization

By: Stefan Axelsson, David Sands

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Hardcover | 19 December 2005

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168 Pages

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23.5 x 16.51 x 1.91

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This monograph is the outgrowth of Stefan Axelson's PhD Dissertation at Chalmers University in Goeteborg, Sweden. The dissertation, in turn collects a number of research efforts performed over a period of six years or so into a coherent whole. It was my honor to serve as the "opponent" at Dr. Axelsson's examination. In the Swedish system, it is the job of the opponent to place the candidate's work into a broader perspective, demonstrating its significance and contributions to the field and then to introduce the work to the attendees at the examination. This done, the candidate presents the technical details of the work and the opponent critiques the work giving the candidate the opportunity to defend it^. This forward is adapted from the introduction that I gave at the examination and should serve to acquaint the reader, not only with the work at hand, but also with the field to which it applies. The title of the work, "Under- standing Intrusion Detection Through Visualization," is particularly telling. As is the case with any good piece of research, we hope to gain an understanding of a problem, not just a recipe or simple solution of immediate, but limited utility. For much of its formative period, computer security concentrated on devel- oping systems that, in effect, embodied a fortress model of protection.

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List of Figures	p. ix
List of Tables	p. xi
Foreword	p. xiii
Preface	p. xvii
Acknowledgments	p. xix
Introduction	p. 1
Context	p. 1
Computer Security	p. 2
Rationale and Problem Statement	p. 3
Information Visualization	p. 4
Overview of the book	p. 5
An Introduction to Intrusion Detection	p. 15
Intrusion Prevention	p. 15
Intrusion Detection	p. 17
The Base-Rate Fallacy and the Difficulty of Intrusion Detection	p. 31
Problems in Intrusion Detection	p. 32
The Base-Rate Fallacy	p. 32
The Base-Rate Fallacy in Intrusion Detection	p. 35
Impact on Intrusion Detection Systems	p. 40
Future Directions	p. 46
Further Reading	p. 46
Conclusions	p. 47
Visualizing Intrusions: Watching the Webserver	p. 49
The Experimental System	p. 50
The Log Reduction Scheme	p. 51
Visualizing the Lowest Scoring Requests	p. 52
Detailed Analysis of the Features Found	p. 56
Effectiveness of the Log Reduction Scheme	p. 59
Discussion	p. 63
Future Work	p. 66
Conclusions	p. 66
Further Reading	p. 67
Combining a Bayesian Classifier with Visualization	p. 69
Automated Learning for Intrusion Detection	p. 69
Naive Bayesian Detection	p. 70
The Experimental Data	p. 71
Visualizing a Bayesian IDS	p. 73
The Training Data	p. 80
The Experiment	p. 80
Conclusions	p. 86
Visualizing the Inner Workings of a Self Learning Classifier	p. 89
Introduction	p. 89
Markovian Matching with Chi Square Testing	p. 90
Visualizing the Detector	p. 92
The Experimental Data	p. 98
Experimental Results	p. 99
Conclusions	p. 109
Future Work	p. 109
Visualization for Intrusion Detection -Hooking the Worm	p. 111
Introduction	p. 111
The Monitored System	p. 112
Scientific Visualization	p. 114
Visual Analysis of the Log File	p. 119
Results of the Investigation	p. 121
Discussion	p. 125
Conclusion	p. 126
Future Work	p. 126
Epilogue	p. 129
Results in Perspective	p. 129
Further Reading	p. 130
Conclusions and Future Work	p. 132
References	p. 133
Author Index	p. 141
Index	p. 143
Table of Contents provided by Ingram. All Rights Reserved.

Understanding Intrusion Detection Through Visualization

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