Loss of user engagement with automation in increasingly complex environments, such as driving a car, can have increasingly unexpected and consequential impacts. This paper presents a three-step process for designing cooperative automation into existing systems to maximize system performance with the constraint of maintaining user engagement. The first step identifies beneficial areas for automation through user testing, focusing on areas where human capabilities constrain performance. Next, an automation is created to perform a portion of the task leading to the constraint. User tests at this point compare different automation types to choose an automation that improves on human-only performance. Lastly, the automation trigger is adjusted to allow the human to maintain engagement with the system at a level desired to avoid the risks associated with loss of user engagement. This process is demonstrated through three human-in-the-loop experiments which illustrate the trade-space between maintaining human engagement and improving system performance.

Smart home devices are relatively inexpensive, readily available, and easily integrated into homes. However, retailers provide smart home devices with little scrutiny in regards to device security or known vulnerabilities. This paper presents a smart home architecture designed with commercially available devices used to investigate Internet of Things data leakage in the wild. Additionally, a pattern-of-life analysis tool was developed to exhibit how an eavesdropper can use traffic from a smart home to classify devices, identify events, track users, and gain physical access to a home. The tool was evaluated through five-days of experimentation in which 17 of 18 devices were classified, 95% of 343 events were identified, and users were tracked with near 100% accuracy. This information, combined with a discovered Bluetooth low energy lock vulnerability, were used to gain unfettered access to the home while the user was away. Furthermore, a mitigation technique was created to introduce spoofed wireless traffic sent on behalf of devices within the home to hinder an eavesdropper’s ability to classify devices, identify events, and track users. During an additional five-day experiment, security devices were concealed, 221 false events were introduced per day, and the user appeared always home. Finally, this paper provides security recommendations to manufacturers and users to help defend against vulnerabilities and create a safer smart home environment.