从源代码到网络配置文件：面向物联网设备的自动化且可追溯的 MUD 配置文件生成

中文摘要 AI 辅助

制造商使用说明（MUD）标准允许物联网制造商在 MUD 文件中定义预期的网络行为。该文件可以被转换为可执行的访问控制策略，限制受损设备仅能通过制造商定义的通信模式进行操作。然而，MUD 的实际采用依赖于准确、完整且可维护的配置文件。现有方法使用基于流量的自动化，但需要设备部署和长时间监控，仅捕获观察期间所表现出的行为。罕见、故障触发或配置相关的通信可能仍然缺失，从而产生不完整的策略，干扰合法操作，并且对每条规则所对应的软件组件提供的洞察有限。我们提出了 AutoMUD，一种源代码驱动的工具，可从物联网设备的固件和软件源代码生成可追溯的 MUD 配置文件。AutoMUD 结合了静态与语法提取、基于检索增强的语言模型推理以及确定性验证和编译，以恢复表征物联网设备的通信行为，并将符合条件的端点转换为策略规则。通过分析代码级证据，AutoMUD 揭示了很少执行的和条件性的通信路径，将每条生成的规则链接到其源代码级出处，并保留被排除的发现及其明确原因以供审查。我们在一个基于 Linux 的代码库上的评估表明，AutoMUD 能够恢复完整的通信行为，将验证过的行为整合到语义端点组中，并生成结构有效的 MUD 配置文件。通过受控的语义故障注入实验，我们证明了 AutoMUD 使分析人员能够检测、定位、解释和纠正传播的错误，从而恢复与其干净版本语义相同的策略。

英文摘要

The Manufacturer Usage Description (MUD) standard allows IoT manufacturers to define expected network behaviors in a MUD file. This file can be translated into enforceable access-control policies, restricting compromised devices to operate solely through manufacturer-defined communication patterns. However, practical adoption of MUD depends on profiles that are accurate, complete, and maintainable. Existing approaches use traffic-based automation but require device deployment and prolonged monitoring, capturing only behavior exercised during observation. Rare, failure-triggered, or configuration-dependent communications may remain absent, producing incomplete policies that disrupt legitimate operation and offer limited insight into the software components responsible for each rule. We present AutoMUD, a source-code-driven tool that generates traceable MUD profiles for IoT devices from their firmware and software source code. AutoMUD combines static and syntactic extraction, retrieval-grounded language-model reasoning, and deterministic validation and compilation to recover the communication behavior characterizing an IoT device and translate eligible endpoints into policy rules. By analyzing code-level evidence, AutoMUD exposes rarely exercised and conditional communication paths, links every generated rule to its source-level provenance, and preserves excluded findings with explicit reasons for review. Our evaluation on a Linux-based repository demonstrates that AutoMUD recovers complete communication behavior, consolidates validated behavior into semantic endpoint groups, and generates structurally valid MUD profiles. Through a controlled semantic fault-injection campaign, we demonstrate that AutoMUD enables analysts to detect, localize, explain, and correct propagated errors, recovering policies semantically identical to their clean counterparts.

发表机构

• University of Padua（帕多瓦大学）
• Eindhoven University of Technology（埃因霍温理工大学）

机构由 AI 辅助整理，请以论文原文为准。