Nonlinear Dynamic Weight-Salp Swarm Algorithm and Long Short-Term Memory with Gaussian Error Linear Units for Network Intrusion Detection System

Network Intrusion Detection Systems (NIDS) are essential for defending cyberattacks, offering vital protection for network security.Recently, there has been a significant growth in the usage of Deep Learning (DL) based algorithms for intrusion detection within the network security.Intrusion detection is a challenging task due to irrelevant or inappropriate features being used for the classification process.In this research, the Nonlinear Dynamic Weight -Salp Swarm Algorithm (NDW-SSA) based feature selection method is employed to select the relevant or appropriate features for classification.The dynamic weight is included in the traditional SSA which enhances the performance of the SSA to select relevant features for classification.Then, the Long Short-Term Memory with Gaussian Error Linear Units (LSTM with GELU) method is developed for the classification of intrusion types.The GELU activation function is utilized during a training process of LSTM which reduces gradient vanishing issue and stabilizes the training process.The NDW-SSA and LSTM with the GELU method obtains 96.78% accuracy on the NSL-KDD dataset, 98.78% accuracy on the UNSW-NB15 dataset, and 98.77% accuracy on the CIC-IDS 2017 dataset, which is superior when compared to Local Search -Pigeon Inspired Optimization (LS-PIO).