Hybrid Deep Learning-Based Model for Intrusion Detection

Abstract

There is an intensive need for intrusion detection systems (IDSs) due to incremental and frequent cyber-attacks. The first line of defense against online threats is an IDS. Researchers are using deep learning (DL) approaches to detect attackers and preserve user information.  In this study, we introduce a hybrid DL-based model. The proposed model integrates LSTM and ResNet to eliminate the vanishing gradient problem and increase the accuracy of the classification model. The proposed model aims to classify between normal or an attack, with each attack either being a DoS, U2R, R2L, or a probe over the NSL-KDD dataset. The proposed model achieves 99.5% according to accuracy. The model was compared with other ML and DL models.