Intrusion Detection Systems Based on RNN and GRU Models using CSE-CIC-IDS2018 Dataset in AWS Cloud
Ferid Kamel, Mohammed Salih Mahdi · Journal of Al-Qadisiyah for Computer Science and Mathematics · 2024
Globally, cloud computing (CC) is becoming a necessary technological advancement. This method is a breakthrough in collaborative services and data storage. Nevertheless, the switch to CC has increased security risks, and the networks and daily interactions we engage in depend on network security. An efficient intrusion detection system is essential as attackers create new attack types and network sizes continue to rise. IDS is dependent primarily on determining whether network packets are malicious or benign. Deep learning algorithms have proved to be effective in detecting intrusions compared to other machine learning methods. In this study, we created deep learning methods to recognize attacks using recurrent neural network (RNN) architecture, namely the GRU (Gated Recurrent Unit) architecture. We use these models to handle binary and multiclass classification on the updated cybersecurity CSE-CIC-IDS2018 dataset. The recommended approach offers superior intrusion detection performance regarding Recall, accuracy, and precision. The recommended procedure yielded accuracy and precision values of 99.92 and 99.685, respectively.