Abstract: Mobile Ad Hoc Networks（MANETs） present more security problem than traditional network for its characteristics such as open medium,dynamic,topology distributed,cooperation and constrained capability.This paper focuses on an integrity detection model,and the supervised anomaly detection classifier of which is based on support vector machines.At the same time,three kinds of pool-based active learning algorithms applied to the model are introduced.Compared with traditional self-learning algorithm,the experiment results show that pool-based active learning algorithms can decrease the request for the number of training instances and reduce the influence on the performance of noise data.It satisfies the demand of high detection rate,high anti-noise and low overhead for MANETs intrusion detection system.

摘要： 由于媒介开放、动态拓扑、交互及资源有限等特点,移动自组网络比传统网络更需要安全保障。介绍了一种集成入侵检测模型。在该模型中带监督异常检测的分类器基于支持向量机。同时,介绍了三种应用在该模型的基于池的主动学习算法。通过与传统的自学习算法比较,显示基于池的主动学习算法能有效地减少对训练样本的依赖,同时减少噪音数据对入侵检测系统性能的影响,适用于移动自组网络对于入侵检测系统高检测率、高抗噪能力和低计算延迟的要求。