Silhouettes: a graphical aid to the interpretation and validation of cluster analysis
Journal of Computational and Applied Mathematics
Self-organization and associative memory: 3rd edition
Self-organization and associative memory: 3rd edition
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Clustering Algorithms
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
Application of Clustering Techniques in a Network Security Testing System
Proceedings of the 2005 conference on Artificial Intelligence Research and Development
IEEE Transactions on Pattern Analysis and Machine Intelligence
Unsupervised case memory organization: analysing computational time and soft computing capabilities
ECCBR'06 Proceedings of the 8th European conference on Advances in Case-Based Reasoning
Multiobjective Evolutionary Clustering Approach to Security Vulnerability Assesments
HAIS '09 Proceedings of the 4th International Conference on Hybrid Artificial Intelligence Systems
Analysis of vulnerability assessment results based on CAOS
Applied Soft Computing
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Security has become a main concern in corporate networks. Security tests are essential to identify vulnerabilities, but experts must analyze very large data and complex information. Unsupervised learning can help by clustering groups of devices with similar vulnerabilities. However an index to evaluate every solution should be calculated to demonstrate results validity. Also the value of the number of clusters should be tuned for every data set in order to find the best solution. This paper introduces SOM as a clustering method to evaluate complex and uncertain knowledge in Consensus, a distributed security system for vulnerability testing; it proposes new metrics to evaluate the cohesion of every cluster, and also the cohesion between clusters; it applies unsupervised algorithms and validity metrics to a security data set; and it presents a method to obtain the best number of clusters regarding these new cohesion metrics: Intracohesion and Intercohesion factors.