Performance Evaluation of Deep Learning Techniques for Wireless Intrusion Detection System: A Comparative Feature Selection Approach

Wireless networks for large-scale data transmission have raised security and privacy concerns in recent years. Thus, intrusion detection systems (IDS) and other precautions have been taken. Computer and network systems need intrusion detection technologies, yet many IDS are ineffective. The increase of feature space also affects the accuracy of machine learning-based IDS methods. Continuous attempts have been made to improve IDS efficacy and dependability. Recently, numerous deep learning methods have been used to improve efficiency. They usually lack feature selection knowledge, resulting in less efficient systems. In this study, a thorough comparative analysis of various feature selection methods have performed in order to identify the most effective set of features to improve the model performance. We analyzed the effectiveness of a variety of feature selection techniques, such as filter, wrapper, and embedded methods in extracting the most informative features from the benchmark NSL-KDD dataset. Subsequently, the selected features were passed to a hybrid deep learning model which incorporated with Convolutional Neural Network (CNN), advanced Recurrent Neural network (RNN) and fully connected Deep Neural Network (DNN) architecture. Our findings show that feature selection method significantly affects model performance. We show that some feature selection strategies regularly provide improved feature subsets, improving model accuracy and robustness by carefully studying their interaction with deep learning algorithms. It is experimented in this study that the selected features by XGBoost shows optimal accuracy of 99.38% in the chosen deep learning method.