Intrusion Detection in Cloud Computing Environments using Deep Learning Algorithms

Abstract

The rise of cloud computing has been attributed to its various advantages, such as its ability to provide on-demand scalability and cost-effectiveness. However, it has also raised concerns about security. Due to the nature of the cloud infrastructure, it can be accessed and shared by multiple users. There are various security threats that can affect the operations and data stored in cloud computing environments. It is therefore important that the security mechanisms are designed to prevent these threats from happening. The paper explores the use of deep learning techniques to detect and prevent unauthorized access to cloud computing environments. Such threats can have a significant impact on the data stored in the cloud and its infrastructure. The NSW_NB15 dataset is a publicly-available collection of information about network traffic and various types of attacks. We use three deep learning techniques to analyze and detect potential threats to cloud computing. These include CNN, RNN, and DNN. The paper presents an evaluation of the performance of the three deep learning algorithms. In particular, the three algorithms performed well in detecting intrusions. The findings of this study suggest that deep learning techniques can help improve the security of cloud environments.