Mining high-speed data streams
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Applications of Data Mining in Computer Security
Applications of Data Mining in Computer Security
Evolutionary Optimization in Dynamic Environments
Evolutionary Optimization in Dynamic Environments
Role mining - revealing business roles for security administration using data mining technology
Proceedings of the eighth ACM symposium on Access control models and technologies
Systematic data selection to mine concept-drifting data streams
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Data Streams: Models and Algorithms (Advances in Database Systems)
Data Streams: Models and Algorithms (Advances in Database Systems)
Fuzzy Multi-Level Security: An Experiment on Quantified Risk-Adaptive Access Control
SP '07 Proceedings of the 2007 IEEE Symposium on Security and Privacy
Inferring higher level policies from firewall rules
LISA'07 Proceedings of the 21st conference on Large Installation System Administration Conference
MLS security policy evolution with genetic programming
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Evaluating role mining algorithms
Proceedings of the 14th ACM symposium on Access control models and technologies
Hierarchical genetic algorithms operating on populations of computer programs
IJCAI'89 Proceedings of the 11th international joint conference on Artificial intelligence - Volume 1
Using hierarchal change mining to manage network security policy evolution
Hot-ICE'11 Proceedings of the 11th USENIX conference on Hot topics in management of internet, cloud, and enterprise networks and services
A Systematic Survey of Self-Protecting Software Systems
ACM Transactions on Autonomous and Adaptive Systems (TAAS) - Special Section on Best Papers from SEAMS 2012
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Recent research [12] has suggested that traditional top down security policy models are too rigid to cope with changes in dynamic operational environments. There is a need for greater flexibility in security policies to protect information appropriately and yet still satisfy operational needs. Previous work has shown that security policies can be learnt from examples using machine learning techniques. Given a set of criteria of concern, one can apply these techniques to learn the policy that best fits the criteria. These criteria can be expressed in terms of high level objectives, or characterised by the set of previously seen decision examples. We argue here that even if an optimal policy could be learnt automatically, it will eventually become sub-optimal over time as the operational requirements change. The policy needs to be updated continually to maintain its optimality. This paper proposes two dynamic security policy learning frameworks