混合量子增强联合学习用于网络攻击检测和检测.
G Subramanian1, M Chinnadurai2
1Department of Computer Science and Engineering, E.G.S. Pillay Engineering College, Nagapattinam, Tamil Nadu, 611002, India. g.subramanian190@gmail.com.
Scientific reports
|December 31, 2024
概括
这项研究引入了一种新的联合学习方法,用于检测网络攻击,增强网络安全. 该方法提高了异常检测准确度和隐私保护,优于传统模型.
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:
- 传统的网络攻击检测方法面临着数据隐私和可扩展性的挑战.
- 集中式系统难以快速适应不断变化的网络威胁.
- 传统方法的局限性需要创新的,分散的解决方案.
研究的目的:
- 开发一种新的联合学习解决方案,用于增强网络攻击检测.
- 解决关于数据隐私和通信开销的集中方法的局限性.
- 提高网络安全系统的适应性和性能.
主要方法:
- 整合一个时空注意力网络 (STAN) 用于模式识别.
- 实施量子启发的联合平均化 (QIFA) 优化程序.
- 使用层次模型聚合和多阶段的精细化,同时保护隐私.
主要成果:
- 拟议的模型实现了高性能指标:98.2%的精度,98.5%的回忆,98.35%的F1得分,98.2%的特异性和98.34%的准确性.
- 与传统的CNN,LSTM,RNN和基线联合学习模型相比,表现出卓越的性能.
- 使用UNSW-NB15数据集有效检测各种网络异常.
结论:
- 新的联合学习方法显著提高了网络攻击检测能力.
- 整合STAN和QIFA提供了一个强大且保护隐私的解决方案.
- 拟议的模型代表了适应性和高效网络安全的重大进步.
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