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中文摘要：
为了实现基于IPv6的异常检测，设计了一种新的高效异常检测模型．针对传统遗传算法编码效率低下的不足进行了改进．该模型使用基于遗传算法的IPv6模糊异常检测规则生成技术，采用Hash函数进行初始种群优化、随机实数编码进行种群编码，提高了检测准确性．使用实时网络数据流对原型系统和Snort进行对比测试，结果表明所提出的模型在检测效率上有明显改善．
英文摘要：
In order to accomplish the anomaly detection based on IPv6, we developed a novel anomaly detection model using a fuzzy anomaly detection rules generation technology for IPv6 with genetic algorithm. In the model, we optimize the initial population using Hash algorithm, encode the population using random real values, and detect the anomaly using fuzzy detection rules. Finally, using the CERNET2 backbone traffic, this paper analyzes the performance of t snort. This study shows that the anomaly detection model he model and compares with the performance of provided in this paper has two advantages： algorithm performance and detection effect.
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