Multiobjective Evolutionary Clustering Approach to Security Vulnerability Assesments

Authors:
G. Corral;A. Garcia-Piquer;A. Orriols-Puig;A. Fornells;E. Golobardes
Affiliations:
Grup de Recerca en Sistemes Intel·ligents La Salle, Universitat Ramon Llull, Barcelona, Spain 08022;Grup de Recerca en Sistemes Intel·ligents La Salle, Universitat Ramon Llull, Barcelona, Spain 08022;Grup de Recerca en Sistemes Intel·ligents La Salle, Universitat Ramon Llull, Barcelona, Spain 08022;Grup de Recerca en Sistemes Intel·ligents La Salle, Universitat Ramon Llull, Barcelona, Spain 08022;Grup de Recerca en Sistemes Intel·ligents La Salle, Universitat Ramon Llull, Barcelona, Spain 08022
Venue:
HAIS '09 Proceedings of the 4th International Conference on Hybrid Artificial Intelligence Systems
Year:
2009

Citing 10
Cited 0

Silhouettes: a graphical aid to the interpretation and validation of cluster analysis

Journal of Computational and Applied Mathematics
Clustering Algorithms

Clustering Algorithms
Self-Organizing Maps

Self-Organizing Maps
Managing a Network Vulnerability Assessment

Managing a Network Vulnerability Assessment
A Systematic Approach to Multi-Stage Network Attack Analysis

IWIA '04 Proceedings of the Second IEEE International Information Assurance Workshop (IWIA'04)
Unsupervised anomaly detection in network intrusion detection using clusters

ACSC '05 Proceedings of the Twenty-eighth Australasian conference on Computer Science - Volume 38
Multi-Objective Evolutionary Optimizations of a Space-Based Reconfigurable Sensor Network under Hard Constraints

BLISS '07 Proceedings of the 2007 ECSIS Symposium on Bio-inspired, Learning, and Intelligent Systems for Security
Matching information security vulnerabilities to organizational security profiles: a genetic algorithm approach

Decision Support Systems - Special issue: Intelligence and security informatics
Cohesion factors: improving the clustering capabilities of consensus

IDEAL'06 Proceedings of the 7th international conference on Intelligent Data Engineering and Automated Learning
An Evolutionary Approach to Multiobjective Clustering

IEEE Transactions on Evolutionary Computation

Quantified Score

Hi-index	0.00

Visualization

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.