微分模糊测试用于检测传感器系统中的篡改及其在核武器核查认证中的应用
Differential fuzz testing to detect tampering in sensor systems and its application to arms control authentication
AI总结：
本文提出了一种物理微分模糊测试方法，用于检测传感器系统中的篡改，通过模拟正常和异常参数空间来检测恶意篡改，应用于核武器核查认证。
摘要：
在未来核武器控制条约中，需要验证核查测量系统的硬件和软件组件，即确保这些系统按预期运行且未被恶意行为者篡改。虽然源代码哈希和静态分析等方法有助于验证软件组件的完整性，但它们可能无法检测环境变量、外部库或辐射测量系统固件和硬件的篡改。本文介绍了物理微分模糊测试的概念，作为一种挑战-响应式篡改指示器，可以同时测试所有上述组件。本质上，我们随机采样（或“模糊”）未篡改系统的参数空间，包括正常和异常参数值，并将输出的时间序列视为系统的基线签名。在未篡改的系统上重新运行相同的输入序列将产生与基线一致的输出序列，而在篡改的系统上运行相同的输入序列将产生修改的输出序列并触发警报。我们随后将该概念应用于核武器验证系统的辐射测量设备认证，并与钠碘化物（NaI）伽马射线谱仪进行了演示模糊测试测量。由于测量输出光谱中存在泊松噪声，我们还使用了用于比较本质上嘈杂或随机的模糊序列的机制。我们证明了物理微分模糊测试可以检测两种类型的篡改尝试，并得出结论，它是一个有前途的框架，用于未来核武器控制、保障等领域中的物理系统认证。
英文摘要：
In future nuclear arms control treaties, it will be necessary to authenticate the hardware and software components of verification measurement systems, i.e., to ensure these systems are functioning as intended and have not been tampered with by malicious actors. While methods such as source code hashing and static analysis can help verify the integrity of software components, they may not be capable of detecting tampering with environment variables, external libraries, or the firmware and hardware of radiation measurement systems. In this article, we introduce the concept of physical differential fuzz testing as a challenge-response-style tamper indicator that can holistically and simultaneously test all the above components in a cyber-physical system. In essence, we randomly sample (or "fuzz") the untampered system's parameter space, including both normal and off-normal parameter values, and consider the time series of outputs as the baseline signature of the system. Re-running the same input sequence on a untampered system will produce an output sequence consistent with this baseline, while running the same input sequence on a tampered system will produce a modified output sequence and raise an alarm. We then apply this concept to authenticating the radiation measurement equipment in nuclear weapon verification systems and conduct demonstration fuzz testing measurements with a sodium iodide (NaI) gamma ray spectrometer. Because there is Poisson noise in the measured output spectra, we also use a mechanism for comparing inherently noisy or stochastic fuzzing sequences. We show that physical differential fuzz testing can detect two types of tamper attempts, and conclude that it is a promising framework for authenticating future cyber-physical systems in nuclear arms control, safeguards, and beyond.
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