Trajectory sampling for direct traffic observation
Proceedings of the conference on Applications, Technologies, Architectures, and Protocols for Computer Communication
Artificial Intelligence: Structures and Strategies for Complex Problem Solving
Artificial Intelligence: Structures and Strategies for Complex Problem Solving
A study of artificial immune systems applied to anomaly detection
A study of artificial immune systems applied to anomaly detection
Using genetic algorithms to explore pattern recognition in the immune system
Evolutionary Computation
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This paper presents a novel architecture for an immunological network anomaly detection system in IPv6 environment, Fuzzy Anomaly Detect System for IPv6 (FADS6). In order to perform the anomaly detection based on IPv6, it is necessary to develop more efficient anomaly detection rules generation technology, genetic algorithm is a good choice. A self-adaptive anomaly detection model was developed using fuzzy detection anomaly algorithm with negative selection of biology and proposed a fuzzy anomaly detection rules generation technology for IPv6 using genetic algorithm. In the proposed model, optimized the initial population with hash algorithm, encoded the population with random real values, and detected the anomaly with fuzzy detection rules. This model is flexible, extendible, and adaptable, can meet the needs, preferences of network administrators and supplied for IPv6 environment. Evaluated the model with CERNET2 backbone traffic, it showed that the model has two advantages: algorithm performance and detection effect, and can be applied to protect the next generation Internet.