KronNet是一种轻量级的Kronecker增强的前神经网络,用于高效的物联网入侵检测
Saeed Ullah1, Junsheng Wu2, Mian Muhammad Kamal3
1School of Software, Northwestern Polytechnical University, Xi'an, 710072, Shaanxi, China.
Scientific reports
|July 1, 2025
概括
新的轻量级入侵检测系统 (IDS) KronNet有效地保护物联网 (IoT) 设备. 这种新的方法为边缘设备的实时威胁检测提供了高精度和最小的资源使用.
科学领域:
- 网络安全 网络安全
- 机器学习 机器学习
- 网络安全 网络安全
背景情况:
- 物联网 (IoT) 设备的普及,由于它们的计算资源有限,给网络安全带来了重大挑战.
- 现有的入侵检测系统 (IDS) 往往难以在这些受限制的环境中满足性能要求.
- 开发轻量级和高效的IDS对于保护不断扩大的物联网生态系统至关重要.
研究的目的:
- 介绍KronNet,一个新的,轻量级的前神经网络,旨在实时检测物联网环境中的入侵.
- 解决物联网安全中阶级不平衡和资源限制的挑战.
- 根据已建立的数据集和最先进的方法评估KronNet的性能和效率.
主要方法:
- 克罗恩网在feed-forward神经网络架构中使用克罗纳克的产品操作.
- 基于高斯混合模型 (GMM) 的过量采样用于减轻阶级不平衡.
- 实现了混合损失函数,将焦点损失和交叉度与自适应类权重相结合.
- 该模型的性能在CICIoT2023和BoT-IoT数据集上得到了验证.
主要成果:
- 克隆网实现了高的检测准确度 (CICIoT2023上的99.01%,BoT-IoT上的99.91%) 和加权的F1分数.
- 该系统显示出异常低的虚假阳性率 (0.03%和0.01%).
- 克隆网的计算开销最小,参数数量低 (例如19.82 KB) 和推断时间快 (例如0.209 ms).
- 量子化后,内存使用量显著减少 (例如,4.96KB),准确性损失微不足道.
结论:
- 在资源有限的物联网环境中,KronNet提供了一个高效和准确的解决方案,用于实时入侵检测.
- 它的轻量级设计和卓越的性能指标使其适合于边缘部署.
- 该模型在计算效率和检测能力方面明显优于现有的最先进方法.
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