Research on Enhancing Cloud Computing Network Security using Artificial Intelligence Algorithms

TL;DR

This paper presents a deep learning-based security framework for cloud computing that improves detection accuracy, response speed, and system availability against evolving cyber threats.

Contribution

It introduces an adaptive, multi-layered security architecture leveraging AI algorithms, which outperforms traditional methods in detecting and responding to cloud security threats.

Findings

Detection accuracy of 97.3% achieved

Average response time of 18 ms

System availability of 99.999%

Abstract

Cloud computing environments are increasingly vulnerable to security threats such as distributed denial-of-service (DDoS) attacks and SQL injection. Traditional security mechanisms, based on rule matching and feature recognition, struggle to adapt to evolving attack strategies. This paper proposes an adaptive security protection framework leveraging deep learning to construct a multi-layered defense architecture. The proposed system is evaluated in a real-world business environment, achieving a detection accuracy of 97.3%, an average response time of 18 ms, and an availability rate of 99.999%. Experimental results demonstrate that the proposed method significantly enhances detection accuracy, response efficiency, and resource utilization, offering a novel and effective approach to cloud computing security.