大数据挖掘的无线传感网络节点入侵风险检测方法

Intrusion risk detection method of wireless sensor network nodes for big data mining

针对无线传感网络节点入侵检测方法难以准确识别,导致风险检测率低的问题,文章提出了基于大数据挖掘的检测方法。该方法通过节点分类、异常特征提取结合AdaBoost算法,实现入侵数据的精准分类;利用簇化网络结构融合数据,经特征选择和归一化处理后,建立基于数据约减和逻辑回归的检测模型;通过处理约减后的数据并计算特征权重,构建判断矩阵,实现准确高效的入侵风险检测。实验结果表明,该方法有效应对网络复杂性,显著提升风险检测率;所提方法能够高效准确地检测入侵风险,为无线传感网络安全稳定运行提供坚实保障。

It is difficult to accurately identify the intrusion detection method of wireless sensor network nodes,which leads to the problem of low risk detection rate.A detection method based on big data mining is proposed.Through node classification,abnormal feature extraction and AdaBoost algorithm,the accurate classification of intrusion data is realized.Using the cluster network structure fusion data,after feature selection and normalization processing,a detection model is built based on data reduction and logistic regression.By processing the reduced data and calculating the feature weights,the judgment matrix is constructed to realize accurate and efficient intrusion risk detection.The experimental results show that the proposed method effectively handles the network complexity and significantly improves the risk detection rate.The experiment proves that it can efficiently and accurately detect the intrusion risk,and provides a solid guarantee for the safe and stable operation of the wireless sensor network.