在联邦计算框架中减轻基于服务的网络攻击的分布式拒绝,使用深度强化学习和式算法进行强化学习
Louai A Maghrabi1, Mahmoud Ragab2, Bandar Alghamdi3
1Department of Software Engineering, College of Engineering, University of Business and Technology, Jeddah, Saudi Arabia.
Scientific reports
|November 17, 2025
概括
本研究介绍了一种新的方法来打击使用联合学习 (FL) 和深度强化学习 (DRL) 的分布式拒绝服务 (DDoS) 攻击. 该技术在检测和分类这些网络威胁方面取得了很高的准确性.
科学领域:
- 网络安全 网络安全
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 分布式拒绝服务 (DDoS) 攻击对互联网基础设施构成重大和持续的威胁.
- 联合学习 (FL) 提供了一种保护隐私的方法,用于分布式数据上的协作模式培训,在网络安全方面获得了引力.
- 深度学习 (DL) 和机器学习 (ML) 对于检测恶意网络流量至关重要,但需要大量,准确的数据集.
研究的目的:
- 提出和评估一种新的技术,即使用深度强化学习和Frilled Lizard优化 (MDDoSFL-DRLFLO) 来缓解联合学习中的DDoS攻击,以提高DDoS攻击的检测和分类.
- 利用FL的协作能力,使用先进的DL技术实时识别威胁.
- 在网络环境中提高DDoS攻击缓解策略的准确性和效率.
主要方法:
- 使用z-score标准化的数据规范化.
- 使用改进的细菌食优化算法 (IBFOA) 进行特征选择.
- 使用决斗双深Q网络 (D3QN) 模型进行分类,通过式优化 (FLO) 方法进行超参数调整.
主要成果:
- MDDoSFL-DRLFLO技术在识别和分类DDoS攻击方面表现出卓越的性能.
- 在CICIDIS 2017和ToN-IoT数据集的实验验证显示了99.52%的高精度.
- 提出的方法在各种评估指标中表现优于现有技术.
结论:
- MDDoSFL-DRLFLO模型通过整合FL,DRL和优化算法有效地减轻DDoS攻击.
- 该研究强调了FL和DRL在开发强大的网络安全解决方案方面的潜力.
- 取得的高精度验证了拟议方法在真实世界威胁检测方面的有效性.
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