走向神经形态的网络双胞胎:在数字双胞胎生态系统中用于认知防御的架构
Nida Nasir1, Hussam Al Hamadi2
1COSYS-LEOST Laboratory, Universit Gustave Eiffel, Lille, France.
Frontiers in big data
|November 20, 2025
概括
这项研究介绍了神经形态网络双胞胎 (NCT),这是一个以大脑为灵感的数字双胞胎网络安全框架. 在虚拟系统中,NCT提供低延迟,适应性防御,以抵御不断变化的网络威胁.
科学领域:
- 网络安全 网络安全
- 人工智能的人工智能
- 计算机工程 计算机工程
背景情况:
- 虚拟化网络物理系统和数字双胞胎越来越复杂,对网络威胁越来越脆弱.
- 传统的安全模型很难适应动态的网络环境和不断变化的攻击模式.
研究的目的:
- 引入神经形态网络双胞胎 (NCT) 框架,用于数字双胞胎的自适应性网络防御.
- 为了提高安全性,利用大脑启发的神经形态原则.
主要方法:
- 该NCT集成尖端神经网络 (SNN) 和事件驱动的认知.
- 神经形态原理如稀疏编码,时间编码和STDP用于处理遥测数据.
- 一个分层的认知架构监测行为,检测异常,并调整防御.
主要成果:
- 原型模拟表明NCT能够实现事件驱动的异常检测和自适应防御.
- 与传统的ML模型相比,NCT在低延迟检测,上下文意识和能源效率方面具有优势.
结论:
- 该NCT框架为虚拟生态系统提供了一个可扩展,自我发展的网络安全范式.
- 潜在的应用包括基础设施监控,自主运输和工业控制系统.
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