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IoTGuard: Dynamic Enforcement of Security and Safety Policy in Commodity IoT
Open accessAbstract
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 triggeraction 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.
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Cited by
IoT security certifications: Challenges and potential approaches
Cirne, A., Sousa, P. R., Resende, J. S., & Antunes, L. (2022) · Computers & Security · doi:10.1016/j.cose.2022.102669 · Cited by 54
Towards Learning-automation IoT Attack Detection through Reinforcement Learning
Gu, T., Abhishek, A., Fu, H., Zhang, H., Basu, D., & Mohapatra, P. (2020) · 2020 IEEE 21st International Symposium on "A World of Wireless, Mobile and Multimedia Networks" (WoWMoM) · doi:10.1109/wowmom49955.2020.00029 · Cited by 50
Metadata
- Authors
- Celik, Z. B., Tan, G., & McDaniel, P.
- Venue
- Proceedings 2019 Network and Distributed System Security Symposium
- Publisher
- Internet Society
- Published
- 2019
- OpenAlex
- W2947175569
- Cited by
- 240
- References
- 0
- FWCI
- 14.92
- Type
- conference paper
- Language
- en
- Open access
- bronze
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- Celik, Z. B., Tan, G., & McDaniel, P. (2019). IoTGuard: Dynamic Enforcement of Security and Safety Policy in Commodity IoT. Proceedings 2019 Network and Distributed System Security Symposium. https://doi.org/10.14722/ndss.2019.23326
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