WSN中数据融合阶段的自适应入侵检测方案

针对基于无线传感网络(WSN)的关键基础设施安全监测问题，提出一种基于数据融合阶段的自适应入侵检测算法。该算法以基于权重的簇化网络结构为基础，利用异常检测子系统和误用检测子系统分别检测已知攻击和未知攻击，然后通过跟踪2个子系统接收操作特征(ROC)和奖惩机制，自动调整转发至2个子系统的融合数据比例，即可实现在数据融合阶段对关键基础设施的自适应入侵检测。仿真分析表明：该算法的准确率和检测率高达99.6%和94.9%以上，与其他经典入侵检测系统相比，可分别至少提高0.5%和10.2%左右。

Adaptive intrusion detection scheme in data fusion stage of WSN

In order to resolve the security monitoring problem of critical infrastructure based on Wireless Sensor Network(WSN), an adaptive intrusion detection scheme in data fusion stage is presented. The presented scheme takes the cluster network structure based on weight as the foundation, respectively using the anomaly detection subsystem and the misuse detection subsystem to detect the known attacks and the unknown attacks, and then adaptively adjusts the proportion of fusion data transmitted to the two sub-systems by tracking the Receiver Operating Characteristics(ROC) and the reward and punishment mechanism of the two sub-systems, so the intrusion detection of critical infrastructure in the data fusion stage can be realized. The simulation analysis indicates that the accuracy rate and detection rate of the presented algorithm is as high as 99.6% and 94.9% respectively, and can be improved at least 0.5% and 10.2% by comparing with other classic intrusion detection systems based on cluster structure.