Citation :

Chaitanya Vootkuri, 2024. "Neural Networks in Cloud Security: Advancing Threat Detection and Automated Response" ESP International Journal of Advancements in Computational Technology (ESP-IJACT)  Volume 2, Issue 2: 142-151.

Abstract :

Cloud computing has dislocated many aspects of technological construction by providing flexible and inexpensive access to solutions. Nevertheless, increased and accelerated cloud adoption with the help of technological developments has become a higher risk of complex cyber threats. This article aims to analyze the use of neural networks in improving cloud computing security, with special reference to threat identification and the capacity for self-organization and response. Neural networks, through the concept of machine learning, can detect deviant behaviors, forecast likely risks, and even counteract in real time. Neural networks employed in cloud environments are described, advances in the topic are discussed, and a state-of-the-art analysis of security enhancement is provided. This research shows that neural networks also improve detection accuracy and substantially reduce the response time of security solutions, which is critical for contemporary cloud protection paradigms.

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Keywords :

Cloud Security, Neural Networks, Threat Detection, Automated Response, Machine Learning, Anomaly Detection, Cybersecurity.