CCS '93 Proceedings of the 1st ACM conference on Computer and communications security
IEEE Transactions on Pattern Analysis and Machine Intelligence
Statistical Pattern Recognition: A Review
IEEE Transactions on Pattern Analysis and Machine Intelligence
Protection in operating systems
Communications of the ACM
Threat Modeling
Looking Back at the Bell-La Padula Model
ACSAC '05 Proceedings of the 21st Annual Computer Security Applications Conference
Data Complexity in Pattern Recognition (Advanced Information and Knowledge Processing)
Data Complexity in Pattern Recognition (Advanced Information and Knowledge Processing)
Classifier ensembles: Select real-world applications
Information Fusion
Security Engineering: A Guide to Building Dependable Distributed Systems
Security Engineering: A Guide to Building Dependable Distributed Systems
Formal concept analysis in information science
Annual Review of Information Science and Technology
On-line privacy and consent: a dialogue, not a monologue
Proceedings of the 2010 workshop on New security paradigms
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This new paradigm defines security policies on cause-effect relations and models security mechanisms in analogy with pattern recognition classifiers. It augments the arsenal of formal computer security evaluation tools with new techniques. A causality model represents possible causes and effects; the causes include threats and the effects may be undesired. Target security policies derived from functional specifications select permitted causalities. Security mechanisms extract features from causes and effects and enforce mechanism-specific policies, approximating the target policy. Advantages of the classifier paradigm are the ability to generalize from incomplete information and examples, to measure classification error and mechanism performance, and to analyze mechanism ensembles and compositions. The classifier paradigm also offers a conception of problem complexity and suggests paying more attention to the impact of mechanisms rather than to their inner workings.