针对深层威胁的深度记忆:一种新的架构,将GRU和IDS的深度学习模型结合起来
Abdulmajeed Alqhatani1, Sajid Mehmood2, Rashid Amin2
1Department of Information Systems, College of Computer Science and Information Systems, Najran University, Najran, Saudi Arabia.
PloS one
|October 15, 2025
概括
一个新的混合深度学习模型,神经图灵机门反复单元 (NTM-GRU),在物联网环境中有效检测复杂的网络威胁,如拒绝服务 (DoS) 和分布式拒绝服务 (DDoS) 攻击. 这种先进的入侵检测系统实现了高准确性和低错误阳性率,以提高网络安全.
科学领域:
- 网络安全和人工智能技术
- 网络入侵检测系统
- 深度学习架构 深度学习架构
背景情况:
- 传统的入侵检测系统 (IDS) 面临着越来越多的网络威胁的数量和复杂性,特别是在物联网 (IoT) 等动态环境中.
- 现有的IDS通常过时,因为它们无法捕捉不断变化的威胁的时间趋势,导致检测复杂,低,慢和零日入侵的局限性.
研究的目的:
- 提出一种新的混合深度学习架构,即神经图灵机门反复单元 (NTM-GRU) 模型,以克服传统IDS的局限性.
- 通过结合外部内存和时间学习来增强入侵检测能力,以分析双重时间尺度并捕捉短期和长期依赖.
主要方法:
- 开发了一种混合深度学习架构,将神经图灵机 (NTM) 的外部内存功能与关闭循环单位 (GRU) 的时间学习能力相结合.
- 在标准数据集 (UNSW-NB15,BoT-IoT) 和现实数据集 (CICIDS2017,CSE-CID-IS2018) 上评估了NTM-GRU模型,用于入侵检测.
- 根据准确性,F1分数,假阳性率和实时推断速度来评估模型的性能.
主要成果:
- 在未知威胁方面,高效率达到99.98%的准确性,F1得分高达96%.
- 证明了低虚假阳性率,低于0.4%,表明威胁识别的高精度.
- 实时推断速度为2.3毫秒,适用于高速网络环境.
- 该模型包含可解释性功能,使其对安全操作中心 (SOC) 有价值.
结论:
- NTM-GRU模型为入侵检测提供了一个可扩展,适应和可解释的解决方案,建立了一个新的最先进的标准.
- 拟议的框架有效地解决了传统IDS在检测当代网络结构 (包括物联网) 中的先进网络威胁方面的不足.
- 混合架构能够分析双重时间尺度并捕捉复杂的依赖关系,这使得它在保护下一代网络方面非常有效.
相关概念视频
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01:23System of Memory
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01:23Storage
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