Random generation of combinatorial structures from a uniform
Theoretical Computer Science
Exact sampling and approximate counting techniques
STOC '98 Proceedings of the thirtieth annual ACM symposium on Theory of computing
On Approximately Counting Colorings of Small Degree Graphs
SIAM Journal on Computing
Self-Nonself Discrimination in a Computer
SP '94 Proceedings of the 1994 IEEE Symposium on Security and Privacy
Theoretical advances in artificial immune systems
Theoretical Computer Science
Foundations of r-contiguous matching in negative selection for anomaly detection
Natural Computing: an international journal
Efficient Algorithms for String-Based Negative Selection
ICARIS '09 Proceedings of the 8th International Conference on Artificial Immune Systems
Negative selection algorithms without generating detectors
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Negative selection algorithms on strings with efficient training and linear-time classification
Theoretical Computer Science
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Negative selection algorithms (NSAs) are immune-inspired anomaly detection schemes that are trained on normal data only: A set of consistent detectors --- i.e., detectors that do not match any element of the training data --- is generated by rejection sampling. Then, input elements that are matched by the generated detectors are classified as anomalous. NSAs generally suffer from exponential runtime. Here, we investigate the possibility to accelerate NSAs by sampling directly from the set of consistent detectors. We identify conditions under which this approach yields fully polynomial time randomized approximation schemes of NSAs with exponentially large detector sets. Furthermore, we prove that there exist detector types for which the approach is feasible even though the only other known method for implementing NSAs in polynomial time fails. These results provide a firm theoretical starting point for implementing efficient NSAs based on modern probabilistic techniques like Markov Chain Monte Carlo approaches.