The number of computers that provide information services via the Internet is constantly increasing, particularly for IoT applications. Compared to the servers in managed data centers, IoT computers have an increased risk of contamination from unidentified computers. It is therefore important for applications that utilize IoT to identify the appropriate computers to use. However, it is difficult to assign digital identifiers with adequate protection to a huge number of IoT computers. In this work, we have devised a method to extract computer-specific features from the characteristics of the CPU core temperature and the drift of the time information. This feature data can be treated as computer-specific information, just like human biometric information. We performed experiments on two types of computer (three of each) with the same software settings and obtained a regression linear equation for each that represents the time deviation per temperature. The correlation coefficients of these equations were greater than 0.9 for all, and a strong positive correlation was obtained. From the equation and the temperature values, we found that it is possible to estimate the computer-specific time deviation. Our method does not require the implementation of a special temperature sensor. Therefore, it shows good potential for future applications.

中文翻译：

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基于时间漂移和 CPU 内核温度的计算机固有特征提取

通过互联网提供信息服务的计算机数量不断增加，尤其是在物联网应用方面。与托管数据中心的服务器相比，物联网计算机被身份不明的计算机污染的风险更高。因此，对于利用 IoT 识别要使用的适当计算机的应用程序来说非常重要。然而，很难为大量物联网计算机分配具有足够保护的数字标识符。在这项工作中，我们设计了一种方法，可以从 CPU 核心温度和时间信息漂移的特征中提取特定于计算机的特征。这些特征数据可以被视为特定于计算机的信息，就像人类生物特征信息一样。我们在具有相同软件设置的两种类型的计算机（每一种）上进行了实验，并获得了每种类型的回归线性方程，表示每个温度的时间偏差。这些方程的相关系数均大于0.9，呈强正相关。从方程和温度值，我们发现可以估计计算机特定的时间偏差。我们的方法不需要实施特殊的温度传感器。因此，它显示出未来应用的良好潜力。从方程和温度值，我们发现可以估计计算机特定的时间偏差。我们的方法不需要实施特殊的温度传感器。因此，它在未来的应用中显示出良好的潜力。从方程和温度值，我们发现可以估计计算机特定的时间偏差。我们的方法不需要实施特殊的温度传感器。因此，它在未来的应用中显示出良好的潜力。