使用属性子集选择与时间卷积网络缓解分布式拒绝服务攻击
Hayam Alamro1, Asmaa Mansour Alghamdi2, Asma Alshuhail3
1Department of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh, 11671, Saudi Arabia.
Scientific reports
|December 2, 2025
概括
本研究介绍了一种使用Salp Swarm-Based Feature Selection和深度学习架构 (IFAD-SSFSDLA) 进行攻击检测的新智能框架,以打击分布式拒绝服务 (DDoS) 攻击. 这种新型模型在实时DDoS攻击检测中实现了高精度.
科学领域:
- 网络安全 网络安全
- 网络安全 网络安全
- 人工智能的人工智能
背景情况:
- 分布式拒绝服务 (DDoS) 攻击对网络基础设施和服务构成重大和不断变化的威胁.
- 现有的方法与现代DDoS攻击的动态模式和复杂性作斗争,需要先进的检测技术.
- 实时识别和缓解DDoS威胁对于防止服务中断和数据泄露至关重要.
研究的目的:
- 提出一种新的智能攻击检测框架,使用Salp Swarm-Based Feature Selection和深度学习架构 (IFAD-SSFSDLA) 进行实时DDoS攻击检测.
- 通过优化功能选择和深度学习,提高DDoS攻击检测的准确性和效率.
- 为识别和减轻日益复杂的DDoS攻击的影响提供强大的解决方案.
主要方法:
- 使用min-max规范化进行数据预处理,用于清理和结构化原始网络流量数据.
- 使用Salp Swarm算法 (SSA) 进行特征选择,以识别和保留最具歧视性的特征,以提高模型性能.
- 使用时间卷积网络 (TCN) 深度学习架构进行攻击分类.
主要成果:
- 国际开发基金-SSFSDLA模型在检测DDoS攻击方面表现出卓越的性能.
- 在CIC-IDS-2017数据集上达到99.56%的高准确率,在Edge-IIoT数据集上达到99.65%.
- 在多个数据集的DDoS攻击检测准确性方面超越现有技术.
结论:
- 拟议的IFAD-SSFSDLA模型为实时DDoS攻击检测提供了一个有效和准确的解决方案.
- 整合Salp Swarm算法用于特征选择和时间卷积网络用于分类,显著提高了检测能力.
- 这一框架在网络安全方面提供了有价值的进步,用于打击普遍和不断变化的网络威胁.
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