An improved DNN model for WLAN intrusion detection

Abstract

Abstract Intrusion detection represents an efficacious approach for addressing security concerns. However, given the substantial volume and high-dimensional nature of WLAN dataset features, existing methods exhibit limited effectiveness in feature extraction, thereby impacting classification performance. To address above problems, an improved deep neural network (DNN) model for WLAN intrusion detection was proposed. Firstly, the activation function and loss function of a single sparse autoencoders (SAE) were determined through experiments, followed by the addition of regularization terms to the autoencoder, to prevent the model from overfitting. Subsequently, multiple SAEs were employed for a stacked architecture. This configuration served the purpose of feature dimension reduction and facilitated the selection of suitable feature dimensions for training the dataset. The chosen features were then utilized as the input layer for a DNN, with a SoftMax classifier serving as the output layer. Secondly, to obtain better DNN model parameters, the grid search method was adopted to optimize the parameters of the DNN model, namely activation, epochs, batch_size, init_mode, and optimizer. The results were visualized for assessment and analysis. Finally, the receiver operating characteristic curves were generated to assess the performance of various models, the analysis results show that the model exhibited better classifier performance.