流量特征选择和分布式拒绝服务在基于机器学习的软件定义网络中检测攻击
Daoqi Han1, Honghui Li1, Xueliang Fu1
1College of Computer and Information Engineering, Inner Mongolia Agricultural University, Hohhot 010018, China.
Sensors (Basel, Switzerland)
|July 13, 2024
概括
本研究引入了一种新的特征选择方法,以增强5G网络的入侵检测系统 (IDS). 该方法提高了DDoS攻击检测速度和准确性,满足5G的需求.
科学领域:
- 网络安全 网络安全
- 网络工程 网络工程
- 机器学习 机器学习
背景情况:
- 5G技术的增长速度和密度加剧了网络安全挑战,特别是软件定义网络 (SDN) 环境中的分布式拒绝服务 (DDoS) 攻击.
- 5G中的网络复杂性可以在入侵检测系统 (IDS) 中引入噪音,从而降低模型概括性和检测性能.
研究的目的:
- 提高5G网络入侵检测系统 (IDS) 的性能,重点是提高检测速度和准确性.
- 提出和验证一种创新的特征选择 (FS) 方法来过网络流量数据.
主要方法:
- 开发了一种创新的特征选择 (FS) 方法,以从网络流量数据中识别代表性和区分特征.
- 在InSDN,CICIDS2017和CICIDS2018数据集上使用四种常见的机器学习 (ML) 模型评估了FS方法的有效性.
- 在模拟平台上进行实时DDoS攻击检测实验.
主要成果:
- 拟议的FS技术显著减少了检测时间,同时保持或提高了DDoS检测准确度.
- 该方法证明适合5G网络要求,确保IDS的高速和可靠性.
- 观察到IDS的稳定性和检测效率有所提高.
结论:
- 新的FS方法有效地解决了复杂的5G网络中DDoS检测的挑战.
- 这种方法提高了IDS性能,使其适合高速,高可靠的5G环境.
- 这种功能选择策略为改善下一代网络网络安全提供了切实可行的解决方案.
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