Abnormal Node Location Algorithm in Multi-source Heterogeneous Network Based on Association Rules

Abstract

Abstract It is difficult to mine association rules between multi-source heterogeneous network nodes, which leads to the high false detection rate, node location error, and location overhead of multi-source heterogeneous network abnormal nodes. Therefore, this article proposes a new abnormal node location algorithm in multi-source heterogeneous network based on association rules. The algorithm of sliding windows multiple value pattern tree is used to extract the association of multi-source heterogeneous network data and realize the mining of association rules between multi-source heterogeneous network nodes. The association rule mining results are preprocessed, and the multi-source heterogeneous network abnormal node location model is built using the multidimensional scaling algorithm and the low rank matrix decomposition model. The model is solved to obtain the multi-source heterogeneous network abnormal node location results. The experimental results show that the false detection rate of abnormal nodes in the proposed algorithm is always below 4.6 %, the maximum node location error is only 2.1 mm, and the average location overhead is 55.1 ms, which can achieve the goal of fast and accurate location of abnormal nodes in multi-source heterogeneous networks.