An Intrusion Detection Method Based on CS-SDAE

Abstract

Intrusion detection is one of the effective methods to prevent network environment from malicious attack. This paper proposes an intrusion detection method based on CS-SDAE to solve the problem of low detection accuracy caused by low traffic feature extraction accuracy and poor robustness in network intrusion detection. Firstly, the structure optimization algorithm of stacked denoising autoencoder (SDAE) is designed based on Cuckoo Search algorithm, and the number of hidden layers and nodes of each layer is optimized by optimizing the detection accuracy of traffic data. Then, the training data are used to train SDAE to minimize the difference between the reconstructed vector of the noisy data and the original input vector, and the characteristics with strong robustness are obtained. Finally, the extracted features are used to train Softmax to build a classifier to detect malicious attacks. Experimental results show that the proposed method can dynamically adjust the structure of SDAE according to the classification task, and the detection accuracy of SDAE is improved by 20.69% compared with traditional SDAE. It has better detection performance.