Capturing Spatio-Temporal Patterns for Intrusion Detection: A Hybrid CNN-LSTM-GRU Model on the NSL-KDD Dataset

Abstract

Intrusion Detection Systems (IDS) are essential for detecting and preventing unauthorized activities in computer networks. This research introduces a DL based IDS framework using the NSL-KDD dataset, employing advanced architectures such as GRU, LSTM, CNN, and a hybrid CNN-LSTM-GRU model. The system addresses both binary and multi-class classification tasks to distinguish between normal and malicious traffic, as well as identify specific attack categories like DoS, Probe, R2L, and U2R. Among the evaluated models, the hybrid CNN-LSTM-GRU approach achieved superior performance due to its ability to capture both spatial and temporal patterns in network data. The results demonstrate that deep learning (DL) significantly enhances the accuracy and robustness of intrusion detection, offering a scalable and intelligent solution for network security.