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Bridging the Semantic Gap: Human Factors in Anomaly-Based Intrusion Detection Systems

  • Richard Harang
Chapter
Part of the Advances in Information Security book series (ADIS, volume 55)

Abstract

Anomaly-based intrusion detection has been pursued as an alternative to standard signature-based methods since the seminal work of Denning in 1987. Despite the length of time for which it has been studied, the high level of activity in this area, and the remarkable success of machine learning techniques in other areas, anomaly-based IDSs remain rarely used in practice, and none appear to have the same widespread popularity as more common misuse detectors such as Bro and Snort. We examine a potential cause of this observation, the “semantic gap” identified by Sommer and Paxson in 2010, in some detail, with reference to several common building blocks for anomaly-based intrusion detection systems. Finally, we revisit tree-based structures for rule construction similar to those first discussed by Vaccaro and Liepins in 1989 in light of modern results in ensemble learning, and suggest how such constructions could be used generate anomaly-based intrusion detection systems that retain acceptable performance while producing output that is more actionable for human analysts.

Keywords

False Positive Rate Intrusion Detection Outlier Detection Anomaly Detection Outlier Detection Method 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  1. 1.ICF InternationalWashingtonUSA

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