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Predictive optimization of DDoS attack mitigation in distributed systems using machine learning
- 1 Electrical and Computer Engineering,University of Illinois Urbana-Champaign
- 2 Computer Science and Technology ,Tianjin University of Technology
- 3 Information Studies,Trine University
- 4 Management Information Systems, University of Pittsburgh
- 5 Computer Science Engineering,Santa Clara University
* Author to whom correspondence should be addressed.
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.
Keywords
Cloud computing, Resource scheduling, Machine learning optimization, Artificial intelligence
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Cite this article
Wang,B.;He,Y.;Shui,Z.;Xin,Q.;Lei,H. (2024). Predictive optimization of DDoS attack mitigation in distributed systems using machine learning. Applied and Computational Engineering,64,94-99.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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Volume title: Proceedings of the 6th International Conference on Computing and Data Science
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