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Improving the Efficiency of Misuse Detection

Authors: Michael Meier 0001; Sebastian Schmerl; Hartmut König;

Improving the Efficiency of Misuse Detection

Abstract

In addition to preventive mechanisms intrusion detection systems (IDS) are an important instrument to protect computer systems. Most IDSs used today realize the misuse detection approach. These systems analyze monitored events for occurrences of defined patterns (signatures), which indicate security violations. Up to now only little attention has been paid to the analysis efficiency of these systems. In particular for systems that are able to detect complex, multi-step attacks not much work towards performance optimizations has been done. This paper discusses analysis techniques of IDSs used today and introduces a couple of optimizing strategies, which exploit structural properties of signatures to increase the analyze efficiency. A prototypical implementation has been used to evaluate these strategies experimentally and to compare them with currently deployed misuse detection techniques. Measurements showed that significant performance improvements can be gained by using the proposed optimizing strategies. The effects of each optimization strategy on the analysis efficiency are discussed in detail.

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    Average
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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Powered by OpenAIRE graph
Found an issue? Give us feedback
selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
12
Average
Top 10%
Top 10%
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