Polar IFS+Parisian Genetic Programming=Efficient IFS Inverse Problem Solving
Genetic Programming and Evolvable Machines
An immunological model of distributed detection and its application to computer security
An immunological model of distributed detection and its application to computer security
Anomaly Detection Using Real-Valued Negative Selection
Genetic Programming and Evolvable Machines
An evolutionary algorithm to generate hyper-ellipsoid detectors for negative selection
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Applying both positive and negative selection to supervised learning for anomaly detection
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Immune anomaly detection enhanced with evolutionary paradigms
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Automated photogrammetric network design using the parisian approach
EC'05 Proceedings of the 3rd European conference on Applications of Evolutionary Computing
An immunity-based technique to characterize intrusions in computernetworks
IEEE Transactions on Evolutionary Computation
EuroGP '09 Proceedings of the 12th European Conference on Genetic Programming
Texture Detection Using Neural Networks Trained on Examples of One Class
AI '09 Proceedings of the 22nd Australasian Joint Conference on Advances in Artificial Intelligence
Temporal defenses for robust recommendations
PSDML'10 Proceedings of the international ECML/PKDD conference on Privacy and security issues in data mining and machine learning
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A major difficulty for anomaly detection lies in discovering boundaries between normal and anomalous behavior, due to the deficiency of abnormal samples in the training phase. In this paper, a novel coevolutionary algorithm which attempts to simulate territory establishment in ecology is conceived to tackle anomaly detection problems. Two species in normal and abnormal behavior pattern space coevolve competitively and cooperatively. Competition prevents individuals in one species from invading the other's territory; cooperation aims to achieve complete pattern coverage by adjusting the evolutionary environment according to the pressure coming from neighbors. In a sense, we extend the definition of cooperative coevolution from "coupled fitness" to "interaction of the evolutionary environment". This coevolutionary algorithm, enhanced with features like niching inside of species, global and local fitness, and fuzzy sets, tries to balance overfitting and overgeneralization. This provides an accurate boundary definition. Experimental results on transactional data from a real financial institution show that this coevolutionary algorithm is more effective than the evolutionary algorithm in evolving normal or abnormal behavior patterns only.