A distributed vulnerability detection system for an intranet

Corral, Guiomar; Zaballos, Agustín; Cadenas, X.; Grane, A.

doi:10.1109/ccst.2005.1594863

Cited by 6 publications

(1 citation statement)

References 5 publications

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“…In order to perform our evaluation of Kohonen Maps and SX-means in a completely unsupervised environment such as data from security tests, we have applied these clustering algorithms to three datasets obtained from Consensus system [9]. These datasets differ in the number and detail of the attributes that describe a case (see Table 1).…”

Section: Testbedmentioning

confidence: 99%

Unsupervised Case Memory Organization: Analysing Computational Time and Soft Computing Capabilities

Fornells

Golobardes

Vernet

et al. 2006

Lecture Notes in Computer Science

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Abstract. There are problems that present a huge volume of information or/and complex data as imprecision and approximated knowledge. Consequently, a Case-Based Reasoning system requires two main characteristics. The first one consists of offering a good computational time without reducing the accuracy rate of the system, specially when the response time is critical. On the other hand, the system needs soft computing capabilities in order to construct CBR systems more tractable, robust and tolerant to noise. The goal of this paper is centred on achieving a compromise between computational time and complex data management by focusing on the case memory organization (or clustering) through unsupervised techniques. In this sense, we have adapted two approaches: 1) neural networks (Kohonen Maps); and 2) inductive learning (X-means). The results presented in this work are based on datasets acquired from medical and telematics domains, and also from UCI repository.

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Section: Testbedmentioning

confidence: 99%