Home-based Internet of Things (IoT) devices have gained in popularity and many households became "smart'' by using devices such as smart sensors, locks, and voice-based assistants. Given the limitations of existing authentication techniques, we explore new opportunities for user authentication in smart home environments. Specifically, we design a novel authentication method in IoT-enabled smart homes. We perform initial experiments and a user study leveraging network traffic collected from 8 IoT devices in our university lab. Preliminary results show that our LSTM model achieves a maximum accuracy of 75% in identifying users.

Phones today carry sensitive information and have a great number of ways to communicate that data. As a result, malware that steal money, information, or simply disable functionality have hit the app stores. Current security solutions for preventing undesirable data leaks are mostly high-overhead and have not been practical enough for smartphones. In this paper, we show that simply monitoring just some instructions (only memory loads and stores) it is possible to achieve low overhead, highly accurate information flow tracking. Our method achieves 98% accuracy (0% false positive and 2% false negative) over DroidBench and was able to successfully catch seven real-world malware instances that steal phone number, location, and device ID using SMS messages and HTTP connections.