RITA自动化识别物联网关键对象并推荐防护策略，保障安全设计
RITA: Automatic Framework for Designing of Resilient IoT Applications
- 用微调的RoBERTa模型从需求文档中自动识别关键设备与服务
- 在7类关键对象中4类表现优于ChatGPT，尤其在传感器与网络资源上
- 完全离线运行，适合对数据隐私敏感的工业物联网场景
构建可靠的物联网系统需识别关键对象（ICOs），如服务、设备和资源，进行威胁分析，并选择缓解策略。然而传统设计流程仍依赖人工，效率低且易出错。尽管工具如ChatGPT可辅助此过程，但存在数据隐私泄露、输出不一致及依赖网络等问题。为此，我们提出RITA——一个开源的自动化框架，采用微调的RoBERTa-NER模型从物联网需求文档中识别ICO，关联威胁并推荐对策。RITA全程离线运行，支持本地部署，保护敏感信息并确保输出一致性，提升标准化水平。在实证评估中，RITA在七类ICO中的四类上超越ChatGPT，特别是在执行器、传感器、网络资源和服务识别方面，使用人工标注与ChatGPT生成的测试数据均验证了其有效性。结果表明，RITA能有效支撑关键安全任务，为构建鲁棒物联网架构提供实用解决方案。
原文摘要 · Abstract (English)
Designing resilient Internet of Things (IoT) systems requires i) identification of IoT Critical Objects (ICOs) such as services, devices, and resources, ii) threat analysis, and iii) mitigation strategy selection. However, the traditional process for designing resilient IoT systems is still manual, leading to inefficiencies and increased risks. In addition, while tools such as ChatGPT could support this manual and highly error-prone process, their use raises concerns over data privacy, inconsistent outputs, and internet dependence. Therefore, we propose RITA, an automated, open-source framework that uses a fine-tuned RoBERTa-based Named Entity Recognition (NER) model to identify ICOs from IoT requirement documents, correlate threats, and recommend countermeasures. RITA operates entirely offline and can be deployed on-site, safeguarding sensitive information and delivering consistent outputs that enhance standardization. In our empirical evaluation, RITA outperformed ChatGPT in four of seven ICO categories, particularly in actuator, sensor, network resource, and service identification, using both human-annotated and ChatGPT-generated test data. These findings indicate that RITA can improve resilient IoT design by effectively supporting key security operations, offering a practical solution for developing robust IoT architectures.
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