用大模型增强上下文推理，让物联网异常检测更智能可解释。
Adaptive and Explainable AI Agents for Anomaly Detection in Critical IoT Infrastructure using LLM-Enhanced Contextual Reasoning
- 结合大模型与记忆缓冲区，动态捕捉数据流中的隐藏模式。
- 在电网和医疗场景测试中，检测准确率显著优于传统方法。
- 结果透明可读，适合对安全性和可解释性要求高的工业应用。
确保关键物联网系统安全稳定运行依赖于快速发现异常。随着智能医疗、能源电网和工业自动化等复杂系统增多，传统检测方法在动态、高维且数据不完整或持续演化环境下的局限性日益凸显。为此，本文提出一种基于大语言模型（LLM）增强的上下文推理框架，融合可解释AI（XAI）代理，提升重要物联网环境中的异常检测能力。该方法利用注意力机制避免逐时步处理细节，通过语义记忆缓冲区识别数据流中的不一致性。实验对比了传统模型与所提方法在真实世界智能电网和医疗场景中的表现，评估指标包括检测准确率、误报率、结果可读性及响应速度。结果显示，新方法在准确性与可解释性方面均显著优于现有模型，具备良好的自适应性与可靠性，适用于未来高要求的物联网异常检测任务。
原文摘要 · Abstract (English)
Ensuring that critical IoT systems function safely and smoothly depends a lot on finding anomalies quickly. As more complex systems, like smart healthcare, energy grids and industrial automation, appear, it is easier to see the shortcomings of older methods of detection. Monitoring failures usually happen in dynamic, high dimensional situations, especially when data is incomplete, messy or always evolving. Such limits point out the requirement for adaptive, intelligent systems that always improve and think. LLMs are now capable of significantly changing how context is understood and semantic inference is done across all types of data. This proposal suggests using an LLM supported contextual reasoning method along with XAI agents to improve how anomalies are found in significant IoT environments. To discover hidden patterns and notice inconsistencies in data streams, it uses attention methods, avoids dealing with details from every time step and uses memory buffers with meaning. Because no code AI stresses transparency and interpretability, people can check and accept the AI's decisions, helping ensure AI follows company policies. The two architectures are put together in a test that compares the results of the traditional model with those of the suggested LLM enhanced model. Important measures to check are the accuracy of detection, how much inaccurate information is included in the results, how clearly the findings can be read and how fast the system responds under different test situations. The metaheuristic is tested in simulations of real world smart grid and healthcare contexts to check its adaptability and reliability. From the study, we see that the new approach performs much better than most existing models in both accuracy and interpretation, so it could be a good fit for future anomaly detection tasks in IoT
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。