An Attack Detection Method for Self-Powered Sensor IoTs Based on Ensemble Learning

Aiming at the problem that machine learning has weak generalization ability and underfitting of minority class samples in network attack identification for the self-powered sensor Internet of Things (IoTs), this article proposes an attack identification method based on ensemble learning. In the proposed method, a two-layer ensemble learning structure consisting of a binary random forest (RF) and the quintuple RF is designed to improve the recognition accuracy; a binary grid search parameter tuning method is proposed to optimize the hyperparameters of the model to deal with the issue of the weak generalization ability of attack recognition; through random attributes combination and ${k}$ -nearest neighbor (KNN) algorithm, the samples of the minor class are generated for unbalanced processing to address the underfitting problem of minority class identification. Gini impurity is adopted as the basis for feature selection, which reduces the complexity of model training and improves the efficiency of identification. The experimental results show that the network attack identification method for self-powered sensor IoTs based on ensemble learning proposed in this article achieves 99.98% in terms of accuracy, precision, recall, and F1 measure.