Silhouettes: a graphical aid to the interpretation and validation of cluster analysis
Journal of Computational and Applied Mathematics
Algorithms for clustering data
Algorithms for clustering data
Clustering Algorithms
Self-Organizing Maps
X-means: Extending K-means with Efficient Estimation of the Number of Clusters
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Cluster ensembles --- a knowledge reuse framework for combining multiple partitions
The Journal of Machine Learning Research
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
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
Statistical Comparisons of Classifiers over Multiple Data Sets
The Journal of Machine Learning Research
Data Clustering: Theory, Algorithms, and Applications (ASA-SIAM Series on Statistics and Applied Probability)
BLISS '07 Proceedings of the 2007 ECSIS Symposium on Bio-inspired, Learning, and Intelligent Systems for Security
Top 10 algorithms in data mining
Knowledge and Information Systems
Computational Intelligence in Information Assurance and Security
Computational Intelligence in Information Assurance and Security
Application of ant K-means on clustering analysis
Computers & Mathematics with Applications
Decision Support Systems - Special issue: Intelligence and security informatics
Multiobjective data clustering
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
Reconciliation engine and metric for network vulnerability assessment
Proceedings of the First International Conference on Security of Internet of Things
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Abstract: Information system security must battle regularly with new threats that jeopardize the protection of those systems. Security tests have to be run periodically not only to identify vulnerabilities but also to control information systems, network devices, services and communications. Vulnerability assessments gather large amounts of data to be further analyzed by security experts, who recently have started using data analysis techniques to extract useful knowledge from these data. With the aim of assisting this process, this work presents CAOS, an evolutionary multiobjective approach to be used to cluster information of security tests. The process enables the clustering of the tested devices with similar vulnerabilities to detect hidden patterns, rogue or risky devices. Two different types of metrics have been selected to guide the discovery process in order to get the best clustering solution: general-purpose and specific-domain objectives. The results of both approaches are compared with the state-of-the-art single-objective clustering techniques to corroborate the benefits of the clustering results to security analysts.