Z. Berkay Celik (Penn State University), Gang Tan (Penn State University), Patrick McDaniel (Penn State University)

Broadly defined as the Internet of Things (IoT), the growth of commodity devices that integrate physical processes with digital connectivity has changed the way we live, play, and work. To date, the traditional approach to securing IoT has treated devices individually. However, in practice, it has been recently shown that the interactions among devices are often the real cause of safety and security violations. In this paper, we present IoTGuard, a dynamic, policy-based enforcement system for IoT, which protects users from unsafe and insecure device states by monitoring the behavior of IoT and trigger-action platform apps. IoTGuard operates in three phases: (a) implementation of a code instrumentor that adds extra logic to an app's source code to collect app's information at runtime, (b) storing the apps' information in a dynamic model that represents the runtime execution behavior of apps, and (c) identifying IoT safety and security policies, and enforcing relevant policies on the dynamic model of individual apps or sets of interacting apps. We demonstrate IoTGuard on 20 flawed apps and find that IoTGuard correctly enforces 12 of the 12 policy violations. In addition, we evaluate IoTGuard on 35 SmartThings IoT and 30 IFTTT trigger-action platform market apps executed in a simulated smart home. IoTGuard enforces 11 unique policies and blocks 16 states in six (17.1%) SmartThings and five (16.6%) IFTTT apps. IoTGuard imposes only 17.3% runtime overhead on an app and 19.8% for five interacting apps. Through this effort, we introduce a rigorously grounded system for enforcing correct operation of IoT devices through systematically identified IoT policies, demonstrating the effectiveness and value of monitoring IoT apps with tools such as IoTGuard.

View More Papers

Giving State to the Stateless: Augmenting Trustworthy Computation with...

Gabriel Kaptchuk (Johns Hopkins University), Matthew Green (Johns Hopkins University), Ian Miers (Cornell Tech)

Read More

NoDoze: Combatting Threat Alert Fatigue with Automated Provenance Triage

Wajih Ul Hassan (NEC Laboratories America, Inc.; University of Illinois at Urbana–Champaign), Shengjian Guo (Virginia Tech), Ding Li (NEC Laboratories America, Inc.), Zhengzhang Chen (NEC Laboratories America, Inc.), Kangkook Jee (NEC Laboratories America, Inc.), Zhichun Li (NEC Laboratories America, Inc.), Adam Bates (University of Illinois at Urbana–Champaign)

Read More

Life after Speech Recognition: Fuzzing Semantic Misinterpretation for Voice...

Yangyong Zhang (Texas A&M University), Lei Xu (Texas A&M University), Abner Mendoza (Texas A&M University), Guangliang Yang (Texas A&M University), Phakpoom Chinprutthiwong (Texas A&M University), Guofei Gu (Texas A&M University)

Read More

NIC: Detecting Adversarial Samples with Neural Network Invariant Checking

Shiqing Ma (Purdue University), Yingqi Liu (Purdue University), Guanhong Tao (Purdue University), Wen-Chuan Lee (Purdue University), Xiangyu Zhang (Purdue University)

Read More