Internet of Things (IoT) devices have grown in popularity since they can directly interact with the real world. Home automation systems automate these interactions. IoT events are crucial to these systems' decision-making but are often unreliable. Security vulnerabilities allow attackers to impersonate events. Using statistical machine learning, IoT event fingerprints from deployed sensors have been used to detect spoofed events. Multivariate temporal data from these sensors has structural and temporal properties that statistical machine learning cannot learn. These schemes' accuracy depends on the knowledge base; the larger, the more accurate. However, the lack of huge datasets with enough samples of each IoT event in the nascent field of IoT can be a bottleneck. In this work, we deployed advanced machine learning to detect event-spoofing assaults. The temporal nature of sensor data lets us discover important patterns with fewer events. Our rigorous investigation of a publicly available real-world dataset indicates that our time-series-based solution technique learns temporal features from sensor data faster than earlier work, even with a 100- or 500-fold smaller training sample, making it a realistic IoT solution.

翻译：物联网设备因其能够直接与现实世界交互而日益普及。家庭自动化系统将这些交互自动化。物联网事件对这些系统的决策至关重要，但往往不可靠。安全漏洞使得攻击者能够伪装事件。利用统计机器学习，已部署传感器产生的物联网事件指纹被用于检测欺骗事件。这些传感器产生的多元时间序列数据具有统计机器学习无法学习的结构和时序特性。这些方案的准确性依赖于知识库；知识库越大，准确性越高。然而，在物联网这一新兴领域，缺乏包含足够每种物联网事件样本的大型数据集可能成为瓶颈。在本工作中，我们部署了先进的机器学习技术来检测事件欺骗攻击。传感器数据的时间特性使我们能够用更少的事件发现重要模式。我们对一个公开可用的真实世界数据集进行的严格研究表明，我们基于时间序列的解决方案技术能够比先前工作更快地从传感器数据中学习时序特征，即使训练样本量小100或500倍，这使其成为一个切实可行的物联网解决方案。