从边缘变压器到物联网决策:用于轻量级入侵检测的卸载嵌入式
Frédéric Adjewa1,2, Moez Esseghir1, Leïla Merghem-Boulahia1
1LIST3N, University of Technology of Troyes, 12 rue Marie Curie, 10010 Troyes CEDEX, France.
Sensors (Basel, Switzerland)
|January 28, 2026
概括
大型语言模型 (LLM) 增强物联网 (IoT) 的安全性. 一个新的系统,SEED,使用优化的LLM来有效地检测资源有限的物联网设备上的入侵,实现高精度.
科学领域:
- 人工智能的人工智能
- 物联网安全 互联网安全
- 机器学习 机器学习
背景情况:
- 人工智能 (AI) 和物联网 (IoT) 的融合为智能应用提供了机会.
- 大型语言模型 (LLM) 为自然语言理解和物联网安全增强提供了高级功能.
- 将计算密集型LLM集成到资源受限的物联网环境中,带来了重大挑战.
研究的目的:
- 检查LLM的适应性以确保物联网生态系统的安全.
- 确定LLM-IoT集成中的关键应用领域和挑战.
- 建议在资源有限的物联网环境中部署LLM的优化策略.
主要方法:
- 引入SEED (Semantic Embeddings for Efficient Detection),这是一个协作嵌入式卸载机制,用于物联网入侵检测.
- 利用轻量级,微调的BERT模型在边缘生成网络嵌入.
- 在终端设备上使用紧的神经网络来查询嵌入式以进行网络流正常性评估.
主要成果:
- 一个优化的BERT模型缩小了约90% (41MB),适合边缘部署.
- 一个紧的神经网络 (137 KB) 专为物联网设备设计.
- 在标准CPU上实现了99.9%的检测准确性,平均推断时间低于70ms.
结论:
- 该SEED系统有效地减轻了在物联网设备上部署LLM的计算负担.
- 拟议的架构利用了资源有限的环境中的LLM分析性能.
- 讨论在动态,对抗性物联网环境中的伦理影响和LLM弹性.
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