Securing Cloud Infrastructures: The Role of Deep Neural Networks in Intrusion Detection

Abstract

of cloud systems has become paramount. This paper investigates the application of deep neural networks (DNNs) in cloud intrusion detection systems (IDS). Traditional IDS systems struggle to handle the massive amount of data generated in cloud environments, often leading to high false-positive rates and missed detections. This paper demonstrates how DNNs can analyze cloud traffic patterns, detect anomalies, and distinguish between benign and malicious activities more efficiently than traditional systems. We examine the architecture of various DNN models used in cloud IDS, including Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN), focusing on their ability to process sequential cloud data and detect subtle patterns indicative of potential attacks. The paper also discusses the deployment of these DNN-based systems in real-time environments and their integration with existing cloud security frameworks. Additionally, it highlights the performance of DNN-based intrusion detection systems in terms of accuracy, recall, and precision, comparing them with legacy IDS systems. Finally, the challenges in adopting DNN models for cloud security are explored, particularly regarding computational overhead, data privacy, and the risk of adversarial attacks.