基于ML的DDoS攻击的检测使用进化算法优化
Fauzia Talpur1, Imtiaz Ali Korejo1, Aftab Ahmed Chandio1
1Institute of Mathematics & Computer Science, University of Sindh, Jamshoro 70680, Sindh, Pakistan.
Sensors (Basel, Switzerland)
|March 13, 2024
概括
这项研究提高了使用进化算法和机器学习的分布式拒绝服务 (DDoS) 攻击检测. 优化的XGB-GA,RF-GA和SVM-GA方法实现了高达99.99%的准确性,改善了网络安全防御.
科学领域:
- 计算机科学 计算机科学
- 网络安全 网络安全
- 人工智能的人工智能
背景情况:
- 现代对信息和通信技术的依赖使系统易受网络攻击的影响.
- 分布式拒绝服务 (DDoS) 攻击是一个普遍的威胁,导致大量停机时间和财务损失.
- 现有的DDoS检测方法显示数量和先前检测率的下降.
研究的目的:
- 通过整合进化优化算法和机器学习来引入DDoS攻击检测的创新方法.
- 提出和评估XGB-GA优化,RF-GA优化和SVM-GA优化方法.
- 为了提高DDoS攻击检测系统的准确性和稳定性.
主要方法:
- 使用与DDoS攻击有关的数据集用于训练机器学习模型 (XGB,RF,SVM).
- 使用进化算法 (EAs) 优化与基于树的管道优化工具 (TPOT) -用于模型优化的遗传编程.
- 实施了十倍交叉验证以评估模型性能.
主要成果:
- 实现了高精度得分:使用XGB-GA达到99.99%,使用RF-GA达到99.50%,使用SVM-GA达到99.99%.
- TPOT认为XGB-GA是构建机器学习模型的最佳算法.
- 与现有的DDoS检测技术相比,显著改进.
结论:
- 拟议的XGB-GA,RF-GA和SVM-GA方法为DDoS攻击检测提供了强大而准确的方法.
- 这项研究通过提高数字基础设施的弹性来推动网络安全领域的发展.
- 进化算法和机器学习的整合为打击普遍的网络威胁提供了强大的工具.
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