A Method for Clustering Web Attacks Using Edit Distance

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dc.creatorPetrovic, Slobodan
dc.creatorAlvarez, Gonzalo
dc.date2003-04-03
dc.date.accessioned2026-07-07T03:19:34Z
dc.date.available2026-07-07T03:19:34Z
dc.descriptionCluster analysis often serves as the initial step in the process of data classification. In this paper, the problem of clustering different length input data is considered. The edit distance as the minimum number of elementary edit operations needed to transform one vector into another is used. A heuristic for clustering unequal length vectors, analogue to the well known k-means algorithm is described and analyzed. This heuristic determines cluster centroids expanding shorter vectors to the lengths of the longest ones in each cluster in a specific way. It is shown that the time and space complexities of the heuristic are linear in the number of input vectors. Experimental results on real data originating from a system for classification of Web attacks are given.
dc.description10 pages, 2 figures, latex format
dc.identifierhttps://arxiv.org/abs/cs/0304007
dc.identifierhttp://arxiv.org/abs/cs/0304007
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/31515
dc.subjectInformation Retrieval
dc.subjectArtificial Intelligence
dc.subjectCryptography and Security
dc.subjectH.3.3;K.6.5
dc.titleA Method for Clustering Web Attacks Using Edit Distance
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