SecureNet-AWMI: Safeguarding Network with Optimal Feature Selection Algorithm

Deep learning has emerged as a leading method for detecting network intrusion threats. However, processing large volumes of data increases computational time costs, and noise in the data can reduce detection rates. To address these challenges, feature selection algorithms are essential for balancing time efficiency and detection accuracy. Feature selection algorithms for intrusion detection systems (IDS) face two primary challenges: selecting the most suitable features for the model and managing data imbalances. Traditional methods often rely on manual selection based on feature importance, which can lead to significant computational errors. And they cannot detect attacks with smaller proportions in complex and variable network traffic. We design a secure network intrusion detection framework SecureNet-AWMI to balance attack distribution by augmenting the low-frequency attack samples and reducing the high-frequency attack samples. The core of SecureNet-AWMI is a feature selection component that uses mutual information theory and adjusts weights to account for different types of attacks. To enhance threat detection and classification, we employ an advanced Convolutional Neural Network (CNN) model enhanced with Bidirectional Long Short-Term Memory (BiLSTM) and an attention mechanism. Comparative experiments on three public datasets – CICIDS2017, UNSW-NB15, and NSL-KDD – show that SecureNet-AWMI outperforms current mainstream feature selection and threat classification techniques.