DCNNBiLSTM: An Efficient Hybrid Deep Learning-Based Intrusion Detection System
Vanlalruata Hnamte, Jamal Hussain
Abstract
In recent years, all real-world processes have been shifted to the cyber environment practically, and computers communicate with one another over the Internet. As a result, there is a rising number of network security vulnerabilities, and network administrators are unable to secure their networks from all forms of cyberattacks. Many techniques for network intrusion detection have also been developed. However, they encounter significant challenges as a result of the ongoing emergence of new vulnerabilities that present systems cannot comprehend. We are motivated by deep learnings exceptional performance in various detection and identification tasks, we present an intelligent and efficient network intrusion detection system (NIDS) based on Deep Learning (DL). In this study, we present a deep learning-based IDS for attack detection. The model has been trained with real-time traffic datasets; CICIDS2018 and Edge_IIoT datasets. The performance of the model is investigated using multiclass classification and achieved a 100% and 99.64% accuracy rate respectively when trained and tested with the datasets.