对不平衡的DDoS流量分类进行适应性抽样框架
Hongjoong Kim1, Deokhyeon Ham1, Kyoung-Sook Moon2
1Department of Mathematics, Korea University, 145 Anam-ro, Seongbuk-gu, Seoul 02841, Republic of Korea.
Sensors (Basel, Switzerland)
|July 12, 2025
概括
在不平衡的数据中检测少数群体对于网络安全至关重要. 我们的自适应采样策略显著改善了分布式拒绝服务 (DDoS) 流量分类,增强了传感器系统中的异常检测.
科学领域:
- 网络安全 网络安全
- 机器学习 机器学习
- 数据科学数据科学数据科学
背景情况:
- 网络安全数据中的类失衡,特别是分布式拒绝服务 (DDoS) 流量,阻碍了对代表性不足的攻击类型的准确检测.
- 现有的机器学习和深度学习模型与不平衡的数据集作斗争,导致性能下降和无效的防御策略.
研究的目的:
- 提出和评估一种适应性抽样策略,以解决DDoS流量分类中的类不平衡问题.
- 改善在不平衡的网络安全数据集中的少数群体类别的检测.
主要方法:
- 开发了一种适应性抽样策略,结合过量抽样和不足抽样技术,以在数据层面重新平衡数据集.
- 在基准DDoS流量数据集上评估了拟议的方法.
主要成果:
- 与基线模型和传统采样方法相比,适应性采样策略显示了较好的分类性能.
- 提高了准确性,回忆力和F1分数等关键指标,特别是在少数群体阶级检测方面.
- 该方法通过增强异常检测能力,提高了传感器驱动的安全系统的可靠性.
结论:
- 拟议的自适应抽样方法为网络安全中不平衡的数据分类提供了强大而可适应的解决方案.
- 这种技术对于改善DDoS流量分类中的少数群体类别检测特别有效.
- 这些发现在模拟传感器环境中具有潜在的应用,需要必要的异常检测.
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