Visualization of anomaly detection using prediction sensitivity
| dc.contributor.author | Laskov, Pavel | |
| dc.contributor.author | Rieck, Konrad | |
| dc.contributor.author | Schäfer, Christin | |
| dc.contributor.author | Müller, Klaus-Robert | |
| dc.contributor.editor | Federrath, Hannes | |
| dc.date.accessioned | 2019-10-11T09:27:55Z | |
| dc.date.available | 2019-10-11T09:27:55Z | |
| dc.date.issued | 2005 | |
| dc.description.abstract | Visualization of learning-based intrusion detection methods is a challenging problem. In this paper we propose a novel method for visualization of anomaly detection and feature selection, based on prediction sensitivity. The method allows an expert to discover informative features for separation of normal and attack instances. Experiments performed on the KDD Cup dataset show that explanations provided by prediction sensitivity reveal the nature of attacks. Application of prediction sensitivity for feature selection yields a major improvement of detection accuracy. | en |
| dc.identifier.isbn | 3-88579-391-1 | |
| dc.identifier.pissn | 1617-5468 | |
| dc.identifier.uri | https://dl.gi.de/handle/20.500.12116/28373 | |
| dc.language.iso | en | |
| dc.publisher | Gesellschaft für Informatik e.V. | |
| dc.relation.ispartof | Sicherheit 2005, Sicherheit – Schutz und Zuverlässigkeit | |
| dc.relation.ispartofseries | Lecture Notes in Informatics (LNI) - Proceedings, Volume P-62 | |
| dc.title | Visualization of anomaly detection using prediction sensitivity | en |
| dc.type | Text/Conference Paper | |
| gi.citation.endPage | 208 | |
| gi.citation.publisherPlace | Bonn | |
| gi.citation.startPage | 197 | |
| gi.conference.date | 5.-8. April 2005 | |
| gi.conference.location | Regensburg | |
| gi.conference.sessiontitle | Regular Research Papers |
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