AnomLocal:一种混合的局部-全球异常检测模型,用于使用联合学习来实现网络安全
1Department of Computer Science College of Computer, Qassim University, Buraydah, Saudi Arabia.
PloS one
|February 2, 2026
概括
通过结合本地和全球学习,在分布式网络中检测异常,AnomLocal增强了网络安全. 这种混合方法提高了准确性和隐私,同时减少了检测时间.
科学领域:
- 网络安全 网络安全
- 网络安全 网络安全
- 机器学习 机器学习
背景情况:
- 分布式网络安全面临着各种数据和复杂攻击的挑战.
- 传统的异常检测系统难以平衡局部准确性和全球通用性.
- 集中式和局部模式存在隐私风险和性能限制.
研究的目的:
- 引入AnomLocal,这是一个用于可扩展,保护隐私和自适应网络保护的混合框架.
- 解决分布式环境中现有的异常检测模型的局限性.
- 提高入侵检测系统的可靠性和效率.
主要方法:
- 开发了AnomLocal,这是一个混合异常检测框架,将本地学习与联合聚合集成.
- 利用一个增强的联邦平均化 (FedAvg) 机制进行全球模型参数聚合.
- 在客户端节点上使用本地数据独立训练神经模型.
主要成果:
- 在UNSW-NB15数据集上,AnomLocal实现了93.5%的准确性,92.8%的精度和91.5%的回忆.
- 超过了集中和独立的本地异常检测模型的性能.
- 降低了25%的检测延迟,使大规模网络能够实时运行.
结论:
- AnomLocal为下一代分布式入侵检测提供了一个强大,可解释和高效的解决方案.
- 该框架有效地将本地数据敏感性与全球适应性统一起来.
- AnomLocal提供了一种保护隐私的方法来保护分布式网络基础设施.
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