基于信息熵的无线传感网入侵检测遗传算法

无线传感网作为正在兴起的物联网的基础设施，在快速发展的同时却面临着多种信息安全风险。提出了一种基于信息熵的无线传感网入侵检测遗传算法，将信息熵和遗传算法应用于检测过程所用比对库的训练，采用异常检测和特征检测结合方法进行入侵检测。仿真实验结果表明，该算法能快速地生成比对库，在入侵检测过程中的收敛性和精确度都有明显改善，其对入侵的检测率高于99．5％，误检率低于0．5％。

Genetic algorithm used in intrusion detection for wireless sensor networks based on information entropy

As the infrastructure of growing Internet of things,wireless sensor networks are encountering with a number of risks of security while they gain a rapid development.A genetic algorithm used in intrusion detection for wireless sensor networks based on information entropy theory is proposed in this paper,which applies the information entropy theory and genetic algorithm to train the comparative library in the process of test.The improved method of intrusion detection combines the anomaly detection module and the feature detection one.The results of simulation show that a comparative library used in intrusion detection can be quickly generated by the proposed algorithm,at the same time,its convergence and precision are outstandingly improved in the process of intrusion detection,the detection rate is more than 99.5% and the false detection rate is less than 0.5% for some intrusion action by this proposed method.