基于机器学习的物联网入侵检测在边缘、雾和云架构中的部署感知可行性框架
A Deployment-Aware Feasibility Framework for Machine Learning-Based IoT Intrusion Detection Across Edge, Fog, and Cloud Architectures
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中文总结 AI 辅助
本文提出一个部署感知的可行性框架，通过定量评分模型评估机器学习入侵检测方法在边缘、雾和云架构中的适用性，为物联网安全设计提供实用指导。
中文摘要 AI 辅助
物联网（IoT）环境的快速增长和异构性暴露了传统基于规则和基于签名的入侵检测系统的根本局限性。本文提出了一种跨边缘、雾/网关和云架构的基于机器学习（ML）的入侵检测方法的定量部署感知分析。与以往主要强调检测准确性的综述不同，本工作定义了从实验和系统级研究中提取的代表性定量部署能力包络，并引入了一个结构化的部署可行性评分（DFS）模型。所提出的框架基于计算需求、内存占用和延迟敏感性，使用加权序数评分机制将ML技术映射到架构层。分析表明，轻量级统计和线性模型最适合边缘部署，集成和基于聚类的方法与雾/网关环境相契合，而深度和优化驱动模型最适合云基础设施。通过定量基础和结构化评估来形式化部署可行性，本工作为在现实架构约束下选择入侵检测解决方案提供了实用指导，支持更明智和部署意识更强的物联网安全设计。
英文摘要
The rapid growth and heterogeneity of Internet of Things (IoT) environments have exposed fundamental limitations in traditional rule-based and signature-based intrusion detection systems. This paper presents a quantitative deployment-aware analysis of machine learning (ML)-based intrusion detection approaches across edge, fog/gateway, and cloud architectures. Unlike prior surveys that primarily emphasize detection accuracy, this work defines representative quantitative deployment capability envelopes extracted from experimental and system-level studies and introduces a structured Deployment Feasibility Score (DFS) model. The proposed framework maps ML techniques to architectural layers based on computational demand, memory footprint, and latency sensitivity using a weighted ordinal scoring mechanism. The analysis demonstrates that lightweight statistical and linear models are most suitable for edge deployment, ensemble and clustering-based methods align with fog/gateway environments, while deep and optimization-driven models are best suited for cloud infrastructures. By formalizing deployment feasibility through quantitative grounding and structured evaluation, this work provides practical guidance for selecting intrusion detection solutions under real-world architectural constraints, supporting more informed and deployment-conscious IoT security design.
发表机构
- Eastern Michigan University（东密歇根大学）
机构由 AI 辅助整理，请以论文原文为准。
