多代理DDOS攻击检测模型:最佳训练的混合分类器和基于的缓解过程
Thiruselvan Palusamy1, Balasubramanian Chelliah1
1Department of Computer Science and Engineering, P.S.R Engineering College, Sivakasi, India.
Network (Bristol, England)
|December 17, 2024
概括
本研究介绍了一种新的多代理系统,用于检测分布式拒绝服务 (DDoS) 攻击. 该系统实现了高精度,增强了对网络威胁的网络安全防御.
科学领域:
- 网络安全 网络安全
- 人工智能的人工智能
- 网络安全 网络安全
背景情况:
- 分布式拒绝服务 (DDoS) 攻击对网络可用性和完整性构成重大威胁.
- 现有的检测方法往往在准确性和效率方面扎,需要先进的解决方案.
研究的目的:
- 提出和评估一个新的多代理系统,以加强DDoS攻击的检测和缓解.
- 研究结构化多剂方法在提高检测准确性和响应时间方面的有效性.
主要方法:
- 一个五阶段的检测模型:预处理,特征提取,维度减少,分类 (使用DBN,Bi-LSTM,深度Maxout与WUJSO优化),以及决策.
- 为了预处理,利用了修改后的双西格形规范化和用于模型调整的混合优化算法 (WUJSO).
- 在多代理框架内集成先进的特征提取和维度减小技术.
主要成果:
- 拟议的多代理系统在90%的学习率下实现了0.953的检测准确性.
- 显著优于现有的方法,如Bi-GRU (0.857),DEEP-MAXOUT (0.910),Bi-LSTM (0.865),RNN (0.814),NN (0.894) 和DBN (0.761). 这种方法的性能明显优于现有的方法.
结论:
- 多代理系统在检测和减轻DDoS攻击方面表现出卓越的有效性.
- 结构化多代理方法为推进强有力的网络安全措施提供了一个有希望的方向.
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