Computer systems that learn: classification and prediction methods from statistics, neural nets, machine learning, and expert systems
On the induction of decision trees for multiple concept learning
On the induction of decision trees for multiple concept learning
C4.5: programs for machine learning
C4.5: programs for machine learning
Artificial Intelligence
Machine learning, neural and statistical classification
Machine learning, neural and statistical classification
Machine Learning
The normative representation of quantified beliefs by belief functions
Artificial Intelligence
Machine Learning
Database Mining: A Performance Perspective
IEEE Transactions on Knowledge and Data Engineering
SLIQ: A Fast Scalable Classifier for Data Mining
EDBT '96 Proceedings of the 5th International Conference on Extending Database Technology: Advances in Database Technology
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This article proposes an intrusion detection and response system using the Smets' transferable belief model (TBM). The system is trained using data on attack classes expressed using Shafer's belief functions, and is hence capable of learning new attacks. Network sensors feed the system belief model before a pignistic model is developed. A risk-driven response subsystem is then generated. The generated system is capable of classifying new intrusion patterns and plan responses to enforce an acceptable risk position as indicated in the corporate security policy.