Bagging decision-making and early warning algorithm fused with temporal features

In this paper, to address the problems of low accuracy and long timeliness of intrusion detection in multi-sensor decision warning technology in security systems, a Fusion_Time_Bagging (FT_Bagging algorithm for short) is proposed, the main feature of which is to participate in multi-sensor decision warning technology by introducing the concept of fused temporal features. The main process of this method is to first discretise the temporal attributes of the training dataset and then assign initial temporal weights; then use a particle swarm optimisation algorithm to optimise the weights of the initial temporal weights to obtain a training dataset with fused temporal features; finally, n sample sets are obtained by self-sampling, and the final prediction model is trained by eliminating the sample sets with temporal weights below the temporal feature threshold. Experiments were conducted on the decision alert dataset and the UCI public dataset, and the results showed that the algorithm outperformed the Bagging and Random Forest algorithms in terms of decision accuracy and timeliness, and outperformed five algorithms including Bagging, Random Forest and XGBoost in terms of performance such as accuracy rate.