LSTM-CNN: a deep learning model for network intrusion detection in cloud infrastructures
Doddi Srilatha, N. Thillaiarasu · International Journal of Critical Infrastructures · 2024
In cloud computing, resources are shared and accessed over the internet to perform intended computations remotely to minimise infrastructure costs. The usage and dependency on the cloud network have increased, and the chances of invasion and loss of data and challenges to develop a reliable intrusion detection and prevention system (IDPS). The existing machine learning-based approaches require the manual extraction of features, which produces low accuracy and high computational time. Providing a secure network involves a framework based on multi-fold validation and privacy in information transmission. The deep learning-based network IDPS model has been proposed to handle the large volume of network traffic in the cloud. This paper proposes a tailored long short-term memory and convolution neural network (LSTM-CNN)-based approach to design a new IDS. The proposed model productively examines intrusions and generates alerts proficiently by incorporating users' information and conducting examinations to detect intrusions. The model's performance is assessed using accuracy, precision, F1-score and recall measures. The proposed model achieves outstanding performance with a test accuracy of 99.27%.