International Journal of Science, Engineering and TechnologyAuthors: Ms. Rajani, Associate Professor Dr. Jyoti
Abstract: Cloud computing has revolutionized data storage, processing, and service delivery, but its dynamic and distributed nature introduces significant cyber security challenges, including data breaches, malware attacks, insider threats, and distributed denial-of-service (DDoS) attacks. Traditional security mechanisms often fail to provide real-time detection and adaptive protection against increasingly sophisticated cyber threats. Deep learning (DL), a subset of artificial intelligence, offers powerful capabilities for identifying complex patterns, detecting anomalies, and predicting potential attacks in large-scale cloud environments. This paper explores the role of deep learning in enhancing cyber security for cloud computing by reviewing DL-based models such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), auto encoders, and generative adversarial networks (GANs). It highlights their application in intrusion detection systems (IDS), malware classification, phishing detection, and threat intelligence. Furthermore, the paper discusses implementation challenges, including data privacy, model interpretability, and computational cost, and proposes future directions for integrating DL with emerging technologies like edge computing and federated learning to achieve robust, scalable, and proactive cloud security solutions
