TY - GEN
T1 - Maverick
T2 - 16th ACM Conference on Security and Privacy in Wireless and Mobile Networks, WiSec 2023
AU - Mazhar, M. Hammad
AU - Li, Li
AU - Hoque, Endadul
AU - Chowdhury, Omar
N1 - Publisher Copyright:
© 2023 Owner/Author.
PY - 2023/5/29
Y1 - 2023/5/29
N2 - Many solutions have been proposed to curb unexpected behavior of automation apps installed on programmable IoT platforms by enforcing safety policies at runtime. However, all prior work addresses a weaker version of the actual problem due to a simpler, unrealistic threat model. These solutions are not general enough as they are heavily dependent on the installed apps and catered to specific IoT platforms. Here, we address a stronger version of the problem via a realistic threat model, where (i) undesired cyber actions can come from not only automation platform backends (e.g., SmartThings) but also close-sourced third-party services (e.g., IFTTT), and (ii) physical actions (e.g., user interactions) on devices can move the IoT system to an undesirable state. We propose a runtime mechanism, dubbed Maverick, which employs an app-independent, platform-agnostic mediator to enforce policies against all undesired cyber actions and applies corrective-actions to bring the IoT system back to a safe state from an unsafe state transition. Maverick is equipped with a policy language capable of expressing rich temporal invariants and an automated toolchain that includes a policy synthesizer and a policy analyzer for user assistance. We implemented Maverick in a prototype and showed its efficacy in both physical and virtual testbeds, incurring minimal overhead.
AB - Many solutions have been proposed to curb unexpected behavior of automation apps installed on programmable IoT platforms by enforcing safety policies at runtime. However, all prior work addresses a weaker version of the actual problem due to a simpler, unrealistic threat model. These solutions are not general enough as they are heavily dependent on the installed apps and catered to specific IoT platforms. Here, we address a stronger version of the problem via a realistic threat model, where (i) undesired cyber actions can come from not only automation platform backends (e.g., SmartThings) but also close-sourced third-party services (e.g., IFTTT), and (ii) physical actions (e.g., user interactions) on devices can move the IoT system to an undesirable state. We propose a runtime mechanism, dubbed Maverick, which employs an app-independent, platform-agnostic mediator to enforce policies against all undesired cyber actions and applies corrective-actions to bring the IoT system back to a safe state from an unsafe state transition. Maverick is equipped with a policy language capable of expressing rich temporal invariants and an automated toolchain that includes a policy synthesizer and a policy analyzer for user assistance. We implemented Maverick in a prototype and showed its efficacy in both physical and virtual testbeds, incurring minimal overhead.
KW - iot systems
KW - policy analysis
KW - policy enforcement
KW - policy synthesis
UR - http://www.scopus.com/inward/record.url?scp=85166205607&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85166205607&partnerID=8YFLogxK
U2 - 10.1145/3558482.3590188
DO - 10.1145/3558482.3590188
M3 - Conference contribution
AN - SCOPUS:85166205607
T3 - WiSec 2023 - Proceedings of the 16th ACM Conference on Security and Privacy in Wireless and Mobile Networks
SP - 73
EP - 84
BT - WiSec 2023 - Proceedings of the 16th ACM Conference on Security and Privacy in Wireless and Mobile Networks
PB - Association for Computing Machinery, Inc
Y2 - 29 May 2023 through 1 June 2023
ER -