A Lightweight Intrusion Detection Model for 5G-enabled Industrial Internet

Aiming at the problem of 5G-enabled Industrial Internet intrusion detection algorithm optimization, this paper proposes a lightweight intrusion detection algorithm based on density-awared fuzzy clustering. Firstly, the algorithm introduces data local density and data feature distance into fuzzy clustering method, which improves the clustering effectiveness and reduces the cluster convergence time. Secondly, the algorithm applies the fuzzy membership degree obtained by the improved fuzzy clustering method as the fuzzy factor for the fuzzy support vector machine to reduce the subjectivity caused by the artificial selection of the fuzzy factor, and minimize the influence of the noise point and the isolated point for the classification. The ICS dataset is used as the experimental data. The theoretical analysis and experimental results show that the proposed intrusion detection algorithm has the characteristics of high detection rate and low computational complexity, and can be applied to the application scenario of 5G-enabled Industrial Internet.