NSL KDD

NSL-KDD is a benchmark dataset widely used to evaluate intrusion detection systems (IDS), focusing on classifying network traffic as normal or malicious. Current research emphasizes improving the accuracy and reliability of IDS models using various techniques, including deep learning architectures like variational autoencoders and recurrent neural networks (RNNs, such as LSTMs and IndRNNs), as well as data augmentation methods like GANs to address class imbalance. These advancements aim to enhance the effectiveness of intrusion detection, leading to more robust and trustworthy network security systems.