Mining in a data-flow environment: experience in network intrusion detection
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Artficial Immune Systems and Their Applications
Artficial Immune Systems and Their Applications
Anomaly Detection over Noisy Data using Learned Probability Distributions
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Snort - Lightweight Intrusion Detection for Networks
LISA '99 Proceedings of the 13th USENIX conference on System administration
Architecture for an Artificial Immune System
Evolutionary Computation
Using genetic algorithms to explore pattern recognition in the immune system
Evolutionary Computation
Learning and optimization using the clonal selection principle
IEEE Transactions on Evolutionary Computation
An artificial immune system architecture for computer securityapplications
IEEE Transactions on Evolutionary Computation
An immunity-based technique to characterize intrusions in computernetworks
IEEE Transactions on Evolutionary Computation
Computational intelligence for network intrusion detection: recent contributions
CIS'05 Proceedings of the 2005 international conference on Computational Intelligence and Security - Volume Part I
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Results of an experimental study of an anomaly detection system based on the paradigm of artificial immune systems (AISs) are presented. Network traffic data are mapped into antibodies or antigenes either by using selected general parameters of the traffic or by using selected protocols headers. Similarities between signatures of attackers and antibodies are measured either using Euclidean distance or normalized Hamming distance. We study the influence of different methods of generation of antibodies and the traffic data coding on the performance of the anomaly detection system.