NDLI: Building a true anomaly detector for intrusion detection

Content Provider	IEEE Xplore Digital Library
Author	Lee, S.C. Heinbuch, D.V.
Copyright Year	2000
Description	Author affiliation: Appl. Phys. Lab., Johns Hopkins Univ., Laurel, MD, USA (Lee, S.C.)
Abstract	While many commercial intrusion defection systems (IDS) are deployed, the protection they afford is modest. At the state-of-the-art, IDS produce voluminous alerts, most false alarms, and function mainly by recognizing the signatures of known attacks so that novel attacks slip past them. Attempts have been made to create systems that recognize the signature of "normal", in the hope that they will then detect attacks, known or novel. These systems are often confounded by the extreme variability of nominal behavior. This paper describes an experiment with an IDS composed of a hierarchy of neural networks (NN) that functions as a true anomaly detector. This result is achieved by monitoring selected areas of network behavior, such as protocols, that are predictable in advance. While this does not cover the entire attack space, a considerable number of attacks are carried out by violating the expectations of the protocol/operating system designer. Within this focus, the NNs are trained using data that spans the entire normal space. These detectors are able to recognize attacks that were not specifically presented during training. We show that using small detectors in a hierarchy gives a better result than a single large detector. Some techniques can be used not only to detect anomalies, but to distinguish among them.
Starting Page	1171
Ending Page	1175
File Size	514736
Page Count	5
File Format	PDF
ISBN	0780365216
DOI	10.1109/MILCOM.2000.904111
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2000-10-22
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Detectors Intrusion detection Neural networks Protocols Monitoring Protection Statistics Network servers Physics Laboratories
Content Type	Text
Resource Type	Article

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