一个联合的变压器增强的双Q网络,用于协作入侵检测检测

Tianqi Ma¹, Yabo Yin², Wenzhong Yang³

¹School of Computer Science and Technology (School of Cyberspace Security), Xinjiang University, Urumqi, 830046, China.

Scientific reports

|December 12, 2025

概括

本研究介绍了FedT-DQN,这是一种用于网络入侵检测的新联合学习方法. 它通过使用变压器和Q网络模型来动态识别威胁来提高准确性和隐私.

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:

物联网 (IoT) 设备的日益普及带来了重大的网络安全挑战.
传统的集中式网络入侵检测系统 (NIDS) 面临隐私问题和复杂的时空数据建模问题.
当前NIDS的局限性需要先进的,保护隐私的解决方案来进行动态威胁检测.

研究的目的:

提出FedT-DQN,一种用于动态网络入侵检测的新型联合强化学习框架.
解决集中式NIDS的局限性,包括隐私风险和不充分的时空相关模型.
为不断增长的物联网生态系统开发一个强大的,保护隐私的入侵检测系统.

主要方法:

开发了FedT-DQN,集成了一个变压器编码器与自我注意力,用于联合聚合.
实现了双层Q网络架构,用于特征提取和入侵检测中的决策优化.
在地方培训中使用软行为者-批判性 (SAC),在联邦环境中适应系统异质性.

主要成果:

在四个基准数据集中实现了高检测准确度,超过[公式:参见文本].
在F1得分上表现出显著的改善,表明更好的检测性能.
成功降低了假阳性率,同时在整个检测过程中确保数据隐私.

结论:

FedT-DQN为动态网络入侵检测提供了一种强大且保护隐私的方法.
该框架有效地模拟时空相关性,并处理系统异质性.
这种方法在保护物联网环境免受网络威胁方面取得了重大进展.

关键词:

双 Q 网络双 Q 网络联合学习是联合学习.侵入检测入侵检测系统强化学习是一种强化学习.变压器变压器变压器

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