一句话：提出 IoTArmor，用机器学习预测性根因分析为 IoT 策略违规选择最优修复动作。
本文关注 IoT 环境中因规则定义不当、应用冲突、设备物理交互或用户无意干扰而可能发生的策略违规，这些违规可能危及敏感信息的机密性与物理/系统完整性。作者提出 IoTArmor：假定违规已被预测将发生，它分析当前系统状态，并利用机器学习模型选择可阻止未来违规的修复动作。该工具不仅生成最优修复方案，还向用户解释修复为何有效，帮助用户理解当前环境缺陷与整体应用配置问题。摘要未报告定量实验结果，主要贡献是预测性根因分析与可解释修复建议的方法设计。
In an Internet of Things (IoT) environment, there are several way things can go wrong based on device activity. Poorly defined rules, conflicts between applications, physical interactions between devices, or unintentional interference by user behavior. Since these devices can have access to sensitive information or the capability to disrupt or harm physical elements in an environment, there is a strong motivation to protect confidentiality and integrity in IoT systems. In this paper we design IoTArmor, a novel Root-Cause-Analysis tool that uses machine learning models to select remediating actions that can prevent violations that would otherwise occur in the future. We assume violations have been predicted to occur and analyze the current system state to produce optimal fixes to prevent the violating behavior. Through this analysis, we can give accurate proposed fixes to prevent the violations, as well as detailed explanations to users as to why the fixes are effective. This methodology provides easily usable information to users about flaws in their environment, both in the current moment and in their overall application setup.
DOI 原文 ·
@inproceedings{paperbot9500,
title = {Better Safe than Sorry: Preventing Policy Violations through Predictive Root-Cause-Analysis for IoT Systems},
author = {Michael Norris and Syed Rafiul Hussain and Gang Tan},
booktitle = {ASE},
year = {2025},
doi = {10.1109/ase63991.2025.00238}
}