Predictive optimization of DDoS attack mitigation in distributed systems using machine learning

Baoming Wang, Yuhang He, Zuwei Shui, Qi Xin, Han Lei
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Abstract

In recent years, cloud computing has been widely used. This paper proposes an innovative approach to solve complex problems in cloud computing resource scheduling and management using machine learning optimization techniques. Through in-depth study of challenges such as low resource utilization and unbalanced load in the cloud environment, this study proposes a comprehensive solution, including optimization methods such as deep learning and genetic algorithm, to improve system performance and efficiency, and thus bring new breakthroughs and progress in the field of cloud computing resource management.Rational allocation of resources plays a crucial role in cloud computing. In the resource allocation of cloud computing, the cloud computing center has limited cloud resources, and users arrive in sequence. Each user requests the cloud computing center to use a certain number of cloud resources at a specific time.
利用机器学习对分布式系统中的 DDoS 攻击缓解进行预测优化
近年来,云计算得到了广泛应用。本文提出了一种利用机器学习优化技术解决云计算资源调度和管理中复杂问题的创新方法。通过对云环境中资源利用率低、负载不均衡等难题的深入研究,本研究提出了包括深度学习、遗传算法等优化方法在内的综合解决方案,以提高系统性能和效率,从而为云计算资源管理领域带来新的突破和进展。在云计算的资源分配中,云计算中心的云资源是有限的,而用户是依次到达的。每个用户请求云计算中心在特定时间使用一定数量的云资源。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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