TY - GEN
T1 - Unveiling IoT Security in Reality
T2 - 33rd USENIX Security Symposium, USENIX Security 2024
AU - Nino, Nicolas
AU - Lu, Ruibo
AU - Zhou, Wei
AU - Lee, Kyu Hyung
AU - Zhao, Ziming
AU - Guan, Le
N1 - Publisher Copyright: © USENIX Security Symposium 2024.All rights reserved.
PY - 2024
Y1 - 2024
N2 - To study the security properties of the Internet of Things (IoT), firmware analysis is crucial. In the past, many works have been focused on analyzing Linux-based firmware. Less known is the security landscape of MCU-based IoT devices, an essential portion of the IoT ecosystem. Existing works on MCU firmware analysis either leverage the companion mobile apps to infer the security properties of the firmware (thus unable to collect low-level properties) or rely on small-scale firmware datasets collected in ad-hoc ways (thus cannot be generalized). To fill this gap, we create a large dataset of MCU firmware for real IoT devices. Our approach statically analyzes how MCU firmware is distributed and then captures the firmware. To reliably recognize the firmware, we develop a firmware signature database, which can match the footprints left in the firmware compilation and packing process. In total, we obtained 8, 432 confirmed firmware images (3, 692 unique) covering at least 11 chip vendors across 7 known architectures and 2 proprietary architectures. We also conducted a series of static analyses to assess the security properties of this dataset. The result reveals three disconcerting facts: 1) the lack of firmware protection, 2) the existence of N-day vulnerabilities, and 3) the rare adoption of security mitigation.
AB - To study the security properties of the Internet of Things (IoT), firmware analysis is crucial. In the past, many works have been focused on analyzing Linux-based firmware. Less known is the security landscape of MCU-based IoT devices, an essential portion of the IoT ecosystem. Existing works on MCU firmware analysis either leverage the companion mobile apps to infer the security properties of the firmware (thus unable to collect low-level properties) or rely on small-scale firmware datasets collected in ad-hoc ways (thus cannot be generalized). To fill this gap, we create a large dataset of MCU firmware for real IoT devices. Our approach statically analyzes how MCU firmware is distributed and then captures the firmware. To reliably recognize the firmware, we develop a firmware signature database, which can match the footprints left in the firmware compilation and packing process. In total, we obtained 8, 432 confirmed firmware images (3, 692 unique) covering at least 11 chip vendors across 7 known architectures and 2 proprietary architectures. We also conducted a series of static analyses to assess the security properties of this dataset. The result reveals three disconcerting facts: 1) the lack of firmware protection, 2) the existence of N-day vulnerabilities, and 3) the rare adoption of security mitigation.
UR - https://www.scopus.com/pages/publications/85204962436
M3 - Conference contribution
T3 - Proceedings of the 33rd USENIX Security Symposium
SP - 5609
EP - 5626
BT - Proceedings of the 33rd USENIX Security Symposium
PB - USENIX Association
Y2 - 14 August 2024 through 16 August 2024
ER -