With the ever increasing use of computers for critical systems, computer security that protects data and computer systems from intentional, malicious intervention, continues to attract attention. Among the methods for defense, the application of a tool to help the operator identify ongoing or already perpetrated attacks (intrusion detection), has been the subject of considerable research in the past ten years. A key problem with current intrusion detection systems is the high number of false alarms they produce. Understanding Intrusion Detection through Visualization presents research on why false alarms are, and will remain a problem; then applies results from the field of information visualization to the problem of intrusion detection. This approach promises to enable the operator to identify false (and true) alarms, while aiding the operator to identify other operational characteristics of intrusion detection systems. This volume presents four different visualization approaches, mainly applied to data from web server access logs. TOC:Foreword by Dr. John McHugh, Canada Research Chair, Director, Privacy and Security Laboratory, Dalhousie University Halifax, N.S. Canada.- Preface.- Introduction.- An Introduction to Intrusion Detection.- The Base-Rate Fallacy and the Difficulty of Intrusion Detection.- Visualising Intrusions: Watching the Webserver.- Combining a Bayesian Classifier with Visualisation.- Visualising the Inner Workings of a Self Learning Classifier.- Visualisation for Intrusion Detection: Hooking the Worm.- References.- Author Index.- Index.
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