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Implicit authentication for traditional objects, such as doors and dumbbells, has rich applications but is rarely studied. An ongoing trend is that traditional objects are retrofitted to smart environments; for instance, a contact sensor is attached to a door to detect door opening (but cannot tell "who is opening the door"). We present the first accurate implicit-authentication system for retrofitted everyday objects, named MoMatch. It makes an authentication decision based on a single natural object use, unlike prior work that requires shaking objects. MoMatch is built on the observation that an object has a motion typically because a human hand moves it; thus, the object's motion and the legitimate user's hand movement should correlate. The main challenge is, given the small amount of data collected during one object use, how to measure the correlation accurately. We convert the correlation measurement problem into an image comparison problem and resolve it using neural networks successfully. MoMatch does not need to profile the user's biometric information and is resilient to mimicry attacks. 

As Internet of Things (IoT) technology becomes more widespread and commonplace in homes, the efficiency of these devices using available bandwidth is becoming more of a concern, as the number of connected devices in a home increase drastically. If each device is controlled using a separate Application Programming Interface (API), the strain on a network will be much worse than it would if all these devices are controlled from a single point. This single point could handle all commands to and from the devices, thereby decreasing the network load. The framework of a testbed presented in this paper will allow developers to build an API around the devices included in the testbed. Then test their algorithms and other research methods from a remote location. 

Due to the increasing complexity of hardware designs, third-party hardware Intellectual Property (IP) cores are often incorporated to alleviate the burden on hardware designers. However, the prevalent use of third-party IPs has raised security concerns such as hardware Trojans. These Trojans inserted in the soft IPs are very difficult to detect through functional testing and no single detection methodology has been able to completely address this issue. Based on a Register- Transfer Level (RTL) soft IP analysis method named Structural Checking, this paper presents a hardware Trojan detection methodology and tool by detailing the implementation of a Golden Reference Library for matching an unknown IP to a functionally similar Golden Reference. The matching result is quantified in percentages so that two different IPs with similar functions have a higher percentage match. A match of the unknown IP to a whitelist IP advances it to be identified with a known functionality, while a match to a blacklist IP causes it to be detected as Trojan-infested.