Network Anomaly Detection Model Based on GSA and DE Optimizing Hybrid Kernel ELM

To enhance the accuracy and generalization of the network intrusion detection model, this study proposes a network intrusion detection model based on the Gravitational Search Algorithm(GSA) and Differential Evolution(DE) algorithm to optimize the hybrid kernel Extreme Learning Machine (ELM).Aiming to improve the weak generalization and poor learning capabilities of ELM models with single kernel function, this model combines the advantages of a polynomial kernel function and radial basis function to construct the so-called Hybrid Kernel ELM(HKELM) model.Furthermore, GSA and DE are combined to optimize the parameters of HKELM, which improves its global and local optimization ability in anomaly detection.Then, the Kernel Principal Component Analysis(KPCA) algorithm is used for data dimensionality reduction and feature extraction from intrusion detection data.Finally, the proposed approach constructs a network intrusion detection model based on GSA and DE optimized hybrid kernel ELM (KPCA-GSADE-HKELM).Experimental results on the KDD99 dataset demonstrate that KPCA-GSADE-HKELM model achievesa higher detection accuracy and faster detection speed compared with KDDwinner, CSVAC, CPSO-SVM, and Dendron models.