Matrix analysis
The 1999 DARPA off-line intrusion detection evaluation
Computer Networks: The International Journal of Computer and Telecommunications Networking - Special issue on recent advances in intrusion detection systems
Artficial Immune Systems and Their Applications
Artficial Immune Systems and Their Applications
Self-Nonself Discrimination in a Computer
SP '94 Proceedings of the 1994 IEEE Symposium on Security and Privacy
A Machine Learning Evaluation of an Artificial Immune System
Evolutionary Computation
An introduction to ROC analysis
Pattern Recognition Letters - Special issue: ROC analysis in pattern recognition
Artificial Life
Combating the Insider Cyber Threat
IEEE Security and Privacy
Autopoiesis, the immune system, and adaptive information filtering
Natural Computing: an international journal
Review: The use of computational intelligence in intrusion detection systems: A review
Applied Soft Computing
Neural network based intrusion detection system for critical infrastructures
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
A detailed analysis of the KDD CUP 99 data set
CISDA'09 Proceedings of the Second IEEE international conference on Computational intelligence for security and defense applications
Clonal selection algorithms: a comparative case study using effective mutation potentials
ICARIS'05 Proceedings of the 4th international conference on Artificial Immune Systems
Immunocomputing for intelligent intrusion detection
IEEE Computational Intelligence Magazine
An artificial immune system architecture for computer securityapplications
IEEE Transactions on Evolutionary Computation
Anomaly detection methods in wired networks: a survey and taxonomy
Computer Communications
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The presented work proposes a new approach for anomaly detection. This approach is based on changes in a population of evolving agents under stress. If conditions are appropriate, changes in the population (modeled by the bioindicators) are representative of the alterations to the environment. This approach, based on an ecological view, improves functionally traditional approaches to the detection of anomalies. To verify this assertion, experiments based on Network Intrussion Detection Systems are presented. The results are compared with the behaviour of other bioinspired approaches and machine learning techniques.