通过机器学习模型,为软件定义的网络网络威胁提供一种高效的基于联合学习的防御机制
Rashid Amin1,2, Antonio Costanzo3, Lial Raja Alzabin4
1Department of Computer Science, University of Engineering and Technology, Taxila, Pakistan. rashid.sdn1@gmail.com.
Scientific reports
|November 21, 2025
概括
本研究介绍了软件定义网络 (SDN) 的人工智能驱动的联合防御系统,以应对复杂的网络攻击. 这种新的方法增强了威胁的检测和响应,提高了网络安全和弹性.
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:
- 软件定义网络 (SDN) 提供了灵活性和集中控制,但由于其架构,容易受到复杂的网络攻击.
- 传统的入侵检测系统与现代威胁的动态,大规模性质作斗争,导致高错误阳性和缓慢的响应.
研究的目的:
- 提出基于人工智能的联合防御系统,以提高SDN环境的弹性和安全性,以应对复杂的网络攻击.
- 提高SDN网络中威胁检测和响应的准确性和速度.
主要方法:
- 开发了一个基于AI的联合防御系统,集成XGBoost用于威胁识别和LightGBM用于实时自适应响应.
- 利用联合学习,在不影响数据保密的情况下,在网络组件之间实现安全的情报共享.
- 采用高维网络流量,日志文件和系统活动的功能工程来增强异常差异化.
主要成果:
- 在基准数据集 (NSL-KDD和CICIDS2017) 上实现了96.3%的检测率.
- 与传统的基于机器学习 (ML) 的入侵检测系统相比,其效率提高了7.8%.
- 显著减少了假阳性和响应时间.
结论:
- 拟议的基于人工智能的联合防御系统提供了一个可扩展,保护隐私和灵活的解决方案,以加强SDN安全性.
- 该框架有效地解决零日威胁,并且非常适合当代SDN环境.
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