基于深度森林算法的分布式WSN入侵检测模型

针对现有的特征选择算法和分类算法在无线传感器网络(WSN)入侵检测系统中检测性能表现不佳、检测实时性差、模型复杂度高等问题,提出一种基于随机森林和深度森林算法的分布式WSN入侵检测模型.该模型首先对传感器节点流量数据进行预处理;然后将轻量级随机森林分类器部署到传感器节点和簇头节点,传感器节点和簇头节点合作对流量数据进行处理,并在基站上采用深度森林算法从大量流量数据中发现攻击行为;最后对WSN中的入侵行为进行实时分类入侵检测.使用无线传感器数据集WSN-DS和NSL-KDD数据集来评估所提出的模型性能.实验结果表明,该模型与现有的入侵检测模型相比,具有良好的检测性能,实时性较高,可避免模型过度拟合.

Distributed WSN intrusion detection model based on deep forest algorithm

In view of the poor performance, poor real-time detection along with higher model complexity of the existing feature selection algorithm and classification algorithm adopted in the wireless sensor network (WSN) intrusion detection system, a distributed WSN intrusion detection model based on the random forest algorithm and the deep forest algorithm is presented in this paper. The proposed model preprocesses traffic data passing through all sensor nodes first, and then allocates lightweight random forest classifier to sensor nodes and cluster head nodes respectively. Sensor nodes and cluster head nodes work up together to deal with the traffic data. Consequently, with the help of the deep forest algorithm emerged attacking behaviors can be seized at a base station from a large amount of traffic data. Intrusion detection can be thus carried out by means real-time classification of intrusion behaviors in WSN. In order to validate effectiveness of the proposed model, we use WSN-DS and NSL-KDD data sets to evaluate the reliability of the proposed model. The experimental results show that, comparing with the existing intrusion detection model, the proposed model does having better detection performance and better real-time performance. The weakness of over fitting in modeling can be avoided as a result.