Zerong Zhao
ABSTRACT
In recent years, neural networks have been used to process network security data in intrusion detection, a process that is often highly nonlinear and strongly correlated. To address the problem that existing methods have high detection rates for known attack types, but have shortcomings in identifying emerging attack types, an intrusion detection (DBN-LSSVM) method combining a deep belief network (DBN) and a least-squares vector machine (LSSVM) is proposed. First, the original network dataset is pre-processed and the DBN is used to downscale the features of the dataset; then the PSO algorithm is used to optimize the input weights and implicit layer biases of LSSVM to establish an intrusion detection model; finally, simulation experiments are conducted on the KDD CUP 99 dataset. The experimental results show that compared with DBN-MSVM, DBN-BP, and SVM methods, the overall detection accuracy is significantly improved, and DBN-LSSVM has higher detection efficiency and better intrusion detection classification performance.
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