  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.
