Stefan Axelsson, David Sands

Understanding Intrusion Detection Through Visualization

• Gebundenes Buch

Produktbeschreibung

This monograph is the outgrowth of Stefan Axelson's PhD Dissertation at Chalmers University in Göteborg, 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," isparticularly 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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Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.

Produktdetails

• Advances in Information Security 24
• Verlag: Springer / Springer US / Springer, Berlin
• Artikelnr. des Verlages: 11425250, 978-0-387-27634-2
• 2006 edition
• Seitenzahl: 145
• Erscheinungstermin: 21. November 2005
• Englisch
• Abmessung: 243mm x 167mm x 15mm
• Gewicht: 406g
• ISBN-13: 9780387276342
• ISBN-10: 0387276343
• Artikelnr.: 15088480

Autorenporträt

Stefan Axelsson, Savedalen, Sweden / David Sands, Chalmers Technical University, Göteborg, Sweden

Inhaltsangabe

An Introduction to Intrusion Detection.- The Base-Rate Fallacy and the Difficulty of Intrusion Detection.- Visualizing Intrusions: Watching the Webserver.- Combining a Bayesian Classifier with Visualization: Understanding the IDS.- Visualizing the Inner Workings of a Self Learning Classifier: Improving the Usability of Intrusion Detection Systems.- Visualization for Intrusion Detection-Hooking the Worm.- Epilogue.