Information Processing Letters
Coverage and Generalization in an Artificial Immune System
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
Generating Optimal Repertoire of Antibody Strings in an Artificial Immune System
Proceedings of the IIS'2000 Symposium on Intelligent Information Systems
An Immunological Approach to Change Detection: Theoretical Results
CSFW '96 Proceedings of the 9th IEEE workshop on Computer Security Foundations
Self-Nonself Discrimination in a Computer
SP '94 Proceedings of the 1994 IEEE Symposium on Security and Privacy
An Immunological Approach to Change Detection: Algorithms, Analysis and Implications
SP '96 Proceedings of the 1996 IEEE Symposium on Security and Privacy
A Sense of Self for Unix Processes
SP '96 Proceedings of the 1996 IEEE Symposium on Security and Privacy
Is negative selection appropriate for anomaly detection?
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Estimating the Support of a High-Dimensional Distribution
Neural Computation
Algorithms on Strings
Revisiting Negative Selection Algorithms
Evolutionary Computation
Theoretical advances in artificial immune systems
Theoretical Computer Science
ACM Computing Surveys (CSUR)
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
Phase transition and the computational complexity of generating r-contiguous detectors
ICARIS'07 Proceedings of the 6th international conference on Artificial immune systems
Negative selection algorithms without generating detectors
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Artificial immune systems for optimisation
Proceedings of the 14th annual conference companion on Genetic and evolutionary computation
A comparative study of negative selection based anomaly detection in sequence data
ICARIS'12 Proceedings of the 11th international conference on Artificial Immune Systems
Efficient negative selection algorithms by sampling and approximate counting
PPSN'12 Proceedings of the 12th international conference on Parallel Problem Solving from Nature - Volume Part I
Artificial immune systems for optimisation
Proceedings of the 15th annual conference companion on Genetic and evolutionary computation
Negative selection algorithm based on grid file of the feature space
Knowledge-Based Systems
| Hi-index | 5.23 |
A string-based negative selection algorithm is an immune-inspired classifier that infers a partitioning of a string space @S^@? into ''normal'' and ''anomalous'' partitions from a training set S containing only samples from the ''normal'' partition. The algorithm generates a set of patterns, called ''detectors'', to cover regions of the string space containing none of the training samples. Strings that match at least one of these detectors are then classified as ''anomalous''. A major problem with existing implementations of this approach is that the detector generating step needs exponential time in the worst case. Here we show that for the two most widely used kinds of detectors, the r-chunk and r-contiguous detectors based on partial matching to substrings of length r, negative selection can be implemented more efficiently by avoiding generating detectors altogether: for each detector type, training set S@?@S^@? and parameter r@?@? one can construct an automaton whose acceptance behaviour is equivalent to the algorithm's classification outcome. The resulting runtime is O(|S|@?r|@S|) for constructing the automaton in the training phase and O(@?) for classifying a string.