The boom of the Internet of Things (IoT) brings great convenience to the society by connecting the physical world to the cyber world, but it also attracts mischievous hackers for benefits. Therefore, understanding potential attacks aiming at IoT systems and devising new protection mechanisms are of great significance to maintain the security and privacy of the IoT ecosystem. In this dissertation, we first demonstrate potential threats against IoT networks and their severe consequences via analyzing a real-world air quality monitoring system. By exploiting the discovered flaws, we can impersonate any victim sensor device and polluting its data with fabricated data. It is a great challenge to fight against runtime software attacks targeting IoT devices based on microcontrollers (MCUs) due to the heterogeneity and constrained computational resources of MCUs. An emerging hardware-based solution is TrustZone-M, which isolates the trusted execution environment from the vulnerable rich execution environment. Though TrustZone-M provides the platform for implementing various protection mechanisms, programming TrustZone-M may introduce a new attack surface. We explore the feasibility of launching five exploits in the context of TrustZone-M and validate these attacks using SAM L11, a Microchip MCU with TrustZone-M enabled. We then propose a security framework for IoT devices using TrustZone-M enabled MCUs, in which device security is protected in five dimensions. The security framework is implemented and evaluated with a full-fledged secure and trustworthy air quality monitoring device using SAM L11 as its MCU. Based on TrustZone-M, a function-based ASLR (fASLR) scheme is designed for runtime software security of IoT devices. fASLR is capable of trapping and modifying control flow upon a function call and randomizing the callee function before its execution. Evaluation results show that fASLR achieves high entropy with low overheads.
| Identifer | oai:union.ndltd.org:ucf.edu/oai:stars.library.ucf.edu:etd2020-2477 |
| Date | 01 January 2022 |
| Creators | Luo, Lan |
| Publisher | STARS |
| Source Sets | University of Central Florida |
| Language | English |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Electronic Theses and Dissertations, 2020- |
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