Efficient Intrusion Detection Model based on LSTM and RNN

Background: Consequently, there is an increase in network security vulnerabilities, making it difficult for network managers to protect their networks against all types of cyberattacks. Numerous methods for detecting network intrusions have also been created. However, the continual appearance of new vulnerabilities that current systems are unable to comprehend presents them with considerable hurdles.Methods: Introduce an improved Deep Learning (DL)-based network intrusion detection system (IDS), inspired by deep learning's remarkable capabilities in a variety of detection and recognition tasks. This paper investigates a hybrid deep learning model that combines Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks to create a versatile and effective security detection system. This system is intended to detect and classify both known and unanticipated cyberattacks.Results: The thorough simulation results show that the system achieves throughput rates of 96.6% and 97.7% for the NSL-KDD and KDDTest+ datasets, respectively. This study provides an efficient way for finding and avoiding intrusions, so effectively addressing security concerns. The outcomes of this kind of research facilitate the process of choosing the best algorithm that may be used to successfully detect upcoming cyberattacks.