相关实验视频

混合AI入侵检测:平衡准确性和效率

Vandit R Joshi¹, Kwame Assa-Agyei¹, Tawfik Al-Hadhrami¹

¹Department of Computer Science, Nottingham Trent University, Nottingham NG1 4FQ, UK.

Sensors (Basel, Switzerland)

|December 31, 2025

概括

这项研究比较了用于物联网 (IoT) 入侵检测的AI模型. CNN-BiLSTM提供高精度,而XGBoost和随机森林为各种物联网需求提供更快,更具竞争力的检测.

科学领域:

网络安全网络安全
人工智能的人工智能
物联网 (IoT) 的物联网 (IoT) 的物联网.

背景情况:

物联网 (IoT) 由于资源限制,协议多样性和基础设施异质性而带来了重大安全挑战.
传统的入侵检测系统 (IDS) 与物联网的规模,互操作性,实时需求,数据隐私和不平衡的流量扎,导致虚假阳性.

研究的目的:

系统地评估和比较用于物联网入侵检测的代表性AI模型的性能和延迟.
在异质物联网环境中,根据准确度-延迟权衡选择合适的AI模型,提供实证见解.

主要方法:

对三种AI模型进行比较分析:卷积神经网络-双向长期短期记忆 (CNN-BiLSTM),随机森林和XGBoost.
对两个基准数据集进行评估:NSL-KDD和UNSW-NB15.
对每个模型的检测性能 (例如,F1得分) 和推断延迟的量化.

主要成果:

CNN-BiLSTM实现了最高的检测能力,F1得分高达0.986,但产生了更高的计算开销.
XGBoost和Random Forest展示了具有竞争力的准确性,其推理延迟显著降低 (在传统硬件上为次毫秒).
在评估的模型中观察到检测准确度和推断延迟之间的明显权衡.

结论:

选择用于物联网入侵检测的AI模型取决于特定应用程序对准确性和速度的要求.
CNN-BiLSTM适用于精度关键的应用,而XGBoost和Random Forest则适用于延迟敏感的场景.
这些发现支持在各种物联网生态系统中提供有效安全的知情部署决策.

关键词:

这是一个双LSTM.物联网安全物联网安全物联网安全这是NSL-KDD.东南大西洋 - - NB1515 卷积神经网络 (CNN) 是一种神经网络.混合型模型混合型模型混合型模型侵入检测系统的入侵检测系统绩效指标绩效指标是一个指标.

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