Owing to the constraints of time and space complexity

support vector machine (SVM) faced with the problem of curse of dimensionality when computation happens in high-dimensional feature space.Therefore

an intrusion detection model of support vector machine based on autoencoder network (AN-SVM) is proposed.First

the multilayer unsupervised restricted boltzmann machine (RBM) in our model is employed in mapping the vector of raw dada from high-dimensional nonlinear space to low-dimensional space

and a mutual mapping autoencoder network of high-dimensional space and low-dimensional space is constructed.Then autoencoder network weights of fine-tuning algorithm based on back propagation network is employed to reconstruct the new optimal high-dimensional representation of data in low-dimensional space

and the corresponding optimal low-dimensional representation of raw data can be obtained.Furthermore

SVM classification algorithm is employed to detect intrusion from the optimal low-dimensional data.The experimental results demonstrate that AN-SVM model can effectively reduce the training time and testing time of classifier in the intrusion detection model and its classification performance outperforms those traditional methods.So

AN-SVM model is a feasible and efficient lightweight intrusion detection model.