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Despite the significant benefits of the widespread adoption of smart home Internet of Things (IoT) devices, these devices are known to be vulnerable to active and passive attacks. Existing literature has demonstrated the ability to infer the activities of these devices by analyzing their network traffic. In this study, we introduce a packet-based signature generation and detection system that can identify specific events associated with IoT devices by extracting simple features from raw encrypted network traffic. Unlike existing techniques that depend on specific time windows, our approach automatically determines the optimal number of packets to generate unique signatures, making it more resilient to network jitters. We evaluate the effectiveness, uniqueness, and correctness of our signatures by training and testing our system using four public datasets and an emulated dataset with varying network delays, verifying known signatures and discovering new ones. Our system achieved an average recall and precision of 98-99% and 98-100%, respectively, demonstrating the effectiveness and feasibility of using packet-level signatures to detect IoT device activities.