Research on Enhancing Cloud Computing Network Security using Artificial Intelligence Algorithms
원문 초록
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.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
GATES: Cost-aware Dynamic Workflow Scheduling via Graph Attention Networks and Evolution Strategy
Enhanced Outsourced and Secure Inference for Tall Sparse Decision Trees
InTec: integrated things-edge computing: a framework for distributing machine learning pipelines in edge AI systems
Persistence of Backdoor-based Watermarks for Neural Networks: A Comprehensive Evaluation
Toward Intelligent and Secure Cloud: Large Language Model Empowered Proactive Defense
All-in-one foundational models learning across quantum chemical levels
토론
이 논문과 연결된 포럼 글이 아직 없습니다.