Trusted execution environments (TEEs), like TrustZone, are pervasively employed to protect security sensitive programs and data from various attacks issued by untrusted rich execution environments (REEs) while they execute compact TEE operating systems which implement minimum security-critical operations but have poor device driver support. In this paper, we propose a twin driver approach where a pair of TEE and REE drivers is generated and cooperate to enable secure and efficient TEE driver support. To begin with, we propose a driver data flow analysis framework named driver analyzer (DrvAna) to automatically analyze the shared states between the TEE and REE driver where a novel data structure named value-type tree is investigated to facilitate field-sensitive data flow analysis upon the driver state. Furthermore, in order to maintain a minimal trusted computing base, we propose a Linux driver runtime (LDR) inside the TEE, a sandbox environment that confines the TEE driver based on the ARM domain access control features and mediates the driver's interaction with the TEE. We implement a DrvAna prototype based on LLVM as well as an LDR prototype on an NXP IMX6Q SABRE-SD evaluation board, adapt 6 existing Linux drivers into LDR, and evaluate their performance. The experimental results show that the LDR drivers can achieve comparable performance with their Linux counterparts with negligible overheads.

For the aspect-based sentiment analysis task, traditional works are only for text modality. However, in social media scenarios, texts often contain abbreviations, clerical errors, or grammatical errors, which invalidate traditional methods. In this study, the cross-model hierarchical interactive fusion network incorporating an end-to-end approach is proposed to address this challenge. In the network, a feature attention module and a feature fusion module are proposed to obtain the multimodal interaction feature between the image modality and the text modality. Through the attention mechanism and gated fusion mechanism, these two modules realize the auxiliary function of image in the text-based aspect-based sentiment analysis task. Meanwhile, a boundary auxiliary module is used to explore the dependencies between two core subtasks of the aspect-based sentiment analysis. Experimental results on two publicly available multi-modal aspect-based sentiment datasets validate the effectiveness of the proposed approach.

The address space layout randomization (ASLR) has been widely deployed on modern operating systems against code reuse attacks (CRAs), such as return-oriented programming (ROP) and jump-oriented programming (JOP). However, porting ASLR to resource-constrained IoT devices is a great challenge due to the limited memory space for randomization. We propose a function-based ASLR scheme (fASLR) for IoT runtime security utilizing the ARM TrustZone-M technology and the memory protection unit (MPU) supported by ARM Cortex-M processors. fASLR loads a function from the flash and randomizes its base address in a randomization region in RAM when the function is being called. We design novel mechanisms on cleaning up finished functions from the RAM and memory addressing to tackle the complexity of function relocation and randomization. Optimizations are applied to effectively reduce overhead introduced by runtime memory management. We also formally prove that user applications will run correctly with fASLR enabled. Compared with the related work, a prominent advantage of fASLR is that fASLR can run an application even if the application code cannot be completely loaded into RAM for execution. We test fASLR with 21 applications. The experimental results show that fASLR achieves a high randomization entropy and incurs a runtime overhead of less than 10%.

Internet of Things (IoT) devices have been increasingly integrated into our daily life. However, such smart devices suffer a broad attack surface. Particularly, attacks targeting the device software at runtime are challenging to defend against if IoT devices use resource-constrained microcontrollers (MCUs). TrustZone-M, a TrustZone extension designed specifically for MCUs, is an emerging hardware security technique fortifying software security of MCU-based IoT devices. This article introduces a comprehensive security framework for IoT devices using TrustZone-M-enabled MCUs, in which device security is protected in five dimensions, i.e., hardware, boot-time software, runtime software, network, and over-the-air (OTA) update. Along developing the framework, we also present the first security analysis of potential runtime software security issues in TrustZone-M-enabled MCUs. In particular, we explore the feasibility of launching stack-based buffer overflow (BOF) attack for code injection, return-oriented programming (ROP) attack, heap-based BOF attack, format string attack, and attacks against nonsecure callable (NSC) functions in the context of TrustZone-M. We validate these attacks using SAM L11, a microchip MCU with TrustZone-M and provide defense mechanisms in the runtime software dimension of the proposed framework. The security framework is implemented with a full-fledged secure and trustworthy air quality monitoring device using SAM L11 as its MCU.

Internet of Things (IoT) devices have been increasingly integrated into our daily life. However, such smart devices suffer a broad attack surface. Particularly, attacks targeting the device software at runtime are challenging to defend against if IoT devices use resource-constrained microcontrollers (MCUs). TrustZone-M, a TrustZone extension for MCUs, is an emerging security technique fortifying MCU based IoT devices. This paper presents the first security analysis of potential software security issues in TrustZone-M enabled MCUs. We explore the stack-based buffer overflow (BOF) attack for code injection, return-oriented programming (ROP) attack, heap-based BOF attack, format string attack, and attacks against Non-secure Callable (NSC) functions in the context of TrustZone-M. We validate these attacks using the Microchip SAM L11 MCU, which uses the ARM Cortex-M23 processor with the TrustZone-M technology. Strategies to mitigate these software attacks are also discussed.