Deep Learning Approaches for Cybersecurity in Hybrid Cloud Infrastructure

Tirumala Ashish Kumar Manne

Abstract: As organizations increasingly adopt hybrid cloud infrastructures to balance flexibility, scalability, and cost-efficiency, the complexity of securing these environments has become a significant challenge. Traditional cybersecurity solutions often struggle to address advanced persistent threats, zero-day vulnerabilities, and the dynamic nature of cloud-based systems. This article explores the application of deep learning (DL) techniques to enhance cybersecurity within hybrid cloud infrastructures. DL models, known for their powerful capabilities in pattern recognition, anomaly detection, and predictive analytics, offer a promising approach to improving threat detection and response times. This paper discusses various DL architectures such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Autoencoders, highlighting their effectiveness in addressing security challenges such as intrusion detection, malware classification, and user behavior analytics. The integration of DL with hybrid cloud environments also involves overcoming several obstacles, including data privacy concerns, computational resource constraints, and the need for real-time processing. Case studies and experimental evaluations demonstrate the practical benefits of DL-driven security systems, with improved accuracy and efficiency compared to traditional machine learning models. Future research directions are proposed, including federated learning, explainable AI, and the evolution of edge-cloud security systems, providing a roadmap for continued advancements in the field.

Keywords: Deep Learning, Cloud Infrastructure, Recurrent Neural Networks (RNNs), Convolutional Neural Networks (CNNs), Federated Learning

How to Cite?: Tirumala Ashish Kumar Manne, "Deep Learning Approaches for Cybersecurity in Hybrid Cloud Infrastructure", Volume 10 Issue 8, August 2021, International Journal of Science and Research (IJSR), Pages: 1320-1333, https://www.ijsr.net/getabstract.php?paperid=SR21807114012, DOI: https://dx.doi.org/10.21275/SR21807114012