# Research on Enhancing Cloud Computing Network Security using Artificial Intelligence Algorithms

arXiv: https://arxiv.org/abs/2502.17801 · HTML: https://mlsanity.com/paper/2502.17801/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 19 references that are in the map.
- First posted on arXiv 25 Feb 2025.

## Builds on (most cited first, 1 of 1)

- [Artificial Intelligence enhanced Security Problems in Real-Time Scenario using Blowfish Algorithm](https://mlsanity.com/paper/2404.09286/) (2024)

## Built on by (most cited first, 4 of 4)

- [Robust Anomaly Detection in Network Traffic: Evaluating Machine Learning Models on CICIDS2017](https://mlsanity.com/paper/2506.19877/) (2025)
- [Research on Edge Computing and Cloud Collaborative Resource Scheduling Optimization Based on Deep Reinforcement Learning](https://mlsanity.com/paper/2502.18773/) (2025)
- [Efficient Temporal Consistency in Diffusion-Based Video Editing with Adaptor Modules: A Theoretical Framework](https://mlsanity.com/paper/2504.16016/) (2025)
- [TDRI: Two-Phase Dialogue Refinement and Co-Adaptation for Interactive Image Generation](https://mlsanity.com/paper/2503.17669/) (2025)

## Abstract (arXiv)

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.