Name: Rahmadi Trimananda
Chair: Professor Brian Demsky
Date: Thursday, August 20, 2020
Time:11:00 AM – 01:00 PM
Committee: Dr. Brian Demsky, Dr. Athina Markopoulou, Dr. Harry Xu
Title: Understanding and Guaranteeing Security, Privacy, and Safety of Smart Homes
Smart homes are becoming increasingly popular. Unfortunately, they come with security, privacy, and safety issues. In this work, we explore new methods and techniques to better understand and guarantee security, privacy, and safety of smart homes. To tackle the existing problems, we view smart home from 3 different sides: devices, platforms, and apps.
On the devices side, we discovered that smart home devices are vulnerable to passive inference attacks based on network traffic, even in the presence of encryption. We first present this passive inference attack and our techniques that we developed to exploit this vulnerability on smart home devices. We created PingPong, a tool that can automatically extract packet-level signatures for device events (e.g., light bulb turning ON/OFF) from network traffic. We evaluated PingPong on popular smart home devices ranging from smart plugs and thermostats to cameras, voice-activated devices, and smart TVs. We were able to: (1) automatically extract previously unknown signa-tures that consist of simple sequences of packet lengths and directions; (2) use those signatures to detect the devices or specific events with an average recall of more than 97%; (3) show that the signatures are unique among hundreds of millions of packets of real world network traffic; (4) show that our methodology is also applicable to publicly available datasets; and (5) demonstrate its robustness in different settings: events triggered by local and remote smartphones, as well as by home-automation systems. Furthermore, we also present existing techniques (e.g., packet padding) as possible defenses against passive inference attacks and their analyses.
On the platforms side, smart home platforms such as SmartThings enable homeowners to manage devices in sophisticated ways to save energy, improve security, and provide conveniences. Unfortunately, we discovered that smart home platforms contain vulnerabilities, potentially impacting home security and privacy. Aside from the traditional defense techniques to enhance the security and privacy of smart home devices, we also created Vigilia, a system that shrinks the attack surface of smart home IoT systems by restricting the network access of devices. As existing smart home systems are closed, we have created an open implementation of a similar programming and configuration model in Vigilia and extended the execution environment to maximally restrict communications by instantiating device-based network permissions. We have implemented and compared Vigilia with forefront IoT-defense systems; our results demonstrate that Vigilia outperforms these systems and incurs negligible overhead.
On the apps side, smart home platforms allow developers to write apps to make smart home devices work together to accomplish tasks, e.g., home security and energy conservation—smart home devices provide the convenience of remotely controlling and automating home appliances. A smarthome app typically implements narrow functionality and thus to fully implement desired function-ality homeowners may need to install multiple apps. These different apps can conflict with each other and these conflicts can result in undesired actions such as locking the door during a fire. We study conflicts between apps on Samsung SmartThings, the most popular platform for developing and deploying smart home IoT devices. By collecting and studying 198 official and 69 third-party apps, we found significant app conflicts in 3 categories: (1) close to 60% of app pairs that access the same device, (2) more than 90% of app pairs with physical interactions, and (3) around 11% of app pairs that access the same global variable. Our results suggest that the problem of conflicts between smart home apps is serious and can create potential safety risks. We then developed an automatic conflict detection tool that uses model checking to automatically detect up to 96% of the conflicts.