The Internet of Things (IoT) appliances often expose sensitive data, either directly or indirectly. They may, for instance, tell whether you are at home right now or what your long or short-term habits are. Therefore, it is crucial to protect such devices against adversaries and has in place an early warning system which indicates compromised devices in a quick and efficient manner. In this paper, we propose time window embedding solutions that efficiently process a massive amount of data and have a low-memory-footprint at the same time. On top of the proposed embedding vectors, we use the core anomaly detection unit. It is a classifier that is based on the transformer’s encoder component followed by a feed-forward neural network. We have compared the proposed method with other classical machine-learning algorithms. Therefore, in the paper, we formally evaluate various machine-learning schemes and discuss their effectiveness in the IoT-related context. Our proposal is supported by detailed experiments that have been conducted on the recently published Aposemat IoT-23 dataset.

物联网（IoT）设备通常直接或间接公开敏感数据。例如，他们可能会告诉您您现在是在家还是长期或短期习惯。因此，至关重要的是要保护此类设备免遭敌人的侵害，并建立一个预警系统，以快速有效的方式指示受损的设备。在本文中，我们提出了时间窗口嵌入解决方案，该解决方案可以有效处理大量数据，同时具有低内存占用量。在建议的嵌入向量之上，我们使用核心异常检测单元。它是一个基于变压器的编码器组件，后跟前馈神经网络的分类器。我们将提出的方法与其他经典的机器学习算法进行了比较。因此，在本文中，我们正式评估了各种机器学习方案，并讨论了它们在与IoT相关的环境中的有效性。我们的建议得到了对最近发布的Aposemat IoT-23数据集进行的详细实验的支持。