Name
Abstract | ||
|---|---|---|
Network vulnerability assessments collect large amounts of data to be further analyzed by security experts. Data mining and, particularly, unsupervised learning can help experts analyze these data and extract several conclusions. This paper presents a contribution to mine data in this security domain. We have implemented an evolutionary multiobjective approach to cluster data of security assessments. Clusters hold groups of tested devices with similar vulnerabilities to detect hidden patterns. Two different metrics have been selected as objectives to guide the discovery process. The results of this contribution are compared with other single-objective clustering approaches to confirm the value of the obtained clustering structures. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1007/978-3-642-02319-4_72 | HAIS |
Keywords | Field | DocType |
different metrics,data mining,cluster data,single-objective clustering approach,multiobjective evolutionary clustering approach,hidden pattern,security expert,security vulnerability assesments,security assessment,evolutionary multiobjective approach,discovery process,security domain,multiobjective optimization,supervised learning,evolutionary algorithm,vulnerability assessment,unsupervised learning,network security | Security domain,Data mining,Vulnerability (computing),Evolutionary algorithm,Vulnerability assessment,Computer science,Network security,Unsupervised learning,Artificial intelligence,Cluster analysis,Business process discovery,Machine learning | Conference |
Volume | ISSN | Citations |
5572 | 0302-9743 | 4 |
PageRank | References | Authors |
0.49 | 10 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| G. Corral | 1 | 33 | 2.42 |
| Alvaro Garcia-Piquer | 2 | 38 | 3.19 |
| Albert Orriols-Puig | 3 | 511 | 25.91 |
| A. Fornells | 4 | 39 | 3.57 |
| Elisabet Golobardes | 5 | 206 | 20.16 |