A massively parallel architecture for a self-organizing neural pattern recognition machine
Computer Vision, Graphics, and Image Processing
Silhouettes: a graphical aid to the interpretation and validation of cluster analysis
Journal of Computational and Applied Mathematics
Bayesian classification (AutoClass): theory and results
Advances in knowledge discovery and data mining
Self-organizing maps
Bottom-Up Induction of Feature Terms
Machine Learning
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Managing a Network Vulnerability Assessment
Managing a Network Vulnerability Assessment
X-means: Extending K-means with Efficient Estimation of the Number of Clusters
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Unsupervised learning techniques for an intrusion detection system
Proceedings of the 2004 ACM symposium on Applied computing
Unsupervised anomaly detection in network intrusion detection using clusters
ACSC '05 Proceedings of the Twenty-eighth Australasian conference on Computer Science - Volume 38
A Case-Based Explanation System for Black-Box Systems
Artificial Intelligence Review
Explanation in Recommender Systems
Artificial Intelligence Review
The Knowledge Engineering Review
Component retrieval using conversational case-based reasoning
Intelligent information processing II
On the role of diversity in conversational recommender systems
ICCBR'03 Proceedings of the 5th international conference on Case-based reasoning: 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
Cohesion factors: improving the clustering capabilities of consensus
IDEAL'06 Proceedings of the 7th international conference on Intelligent Data Engineering and Automated Learning
Detecting unknown attacks in wireless sensor networks using clustering techniques
HAIS'11 Proceedings of the 6th international conference on Hybrid artificial intelligent systems - Volume Part I
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Network security tests should be periodically conducted to detect vulnerabilities before they are exploited. However, analysis of testing results is resource intensive with many data and requires expertise because it is an unsupervised domain. This paper presents how to automate and improve this analysis through the identification and explanation of device groups with similar vulnerabilities. Clustering is used for discovering hidden patterns and abnormal behaviors. Self-organizing maps are preferred due to their soft computing capabilities. Explanations based on anti-unification give comprehensive descriptions of clustering results to analysts. This approach is integrated in Consensus, a computer-aided system to detect network vulnerabilities.