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.

Computer security - the protection of data and computer systems from intentional, malicious intervention - is attracting increasing attention. Much work has gone into development of tools to detect ongoing or already perpetrated attacks, but a key shortfall in current intrusion detection systems is the high number of false alarms they produce. This book analyzes the false alarm problem, then applies results from the field of information visualization to the problem of intrusion detection. Four different visualization approaches are presented, mainly applied to data from web server access logs.