arXiv subjects

Yong Guan

Publications and source records attributed to Yong Guan.

22 records · Page 2Linked to original sources

arXiv2019

MicroTEE: Designing TEE OS Based on the Microkernel Architecture

ARM TrustZone technology is widely used to provide Trusted Execution Environments (TEE) for mobile devices. However, most TEE OSes are implemented as monolithic kernels. In such designs, device drivers, kernel services and kernel modules all run in the kernel, which results in large size of the kernel. It is difficult to guarantee that all components of the kernel have no security vulnerabilities in the monolithic kernel architecture, such as the integer overflow vulnerability in Qualcomm QSEE TrustZone and the TZDriver vulnerability in HUAWEI Hisilicon TEE architecture. This paper presents MicroTEE, a TEE OS based on the microkernel architecture. In MicroTEE, the microkernel provides strong isolation for TEE OS's basic services, such as crypto service and platform key management service. The kernel is only responsible for providing core services such as address space management, thread management, and inter-process communication. Other fundamental services, such as crypto service and platform key management service are implemented as applications at the user layer. Crypto Services and Key Management are used to provide Trusted Applications (TAs) with sensitive information encryption, data signing, and platform attestation functions. Our design avoids the compromise of the whole TEE OS if only one kernel service is vulnerable. A monitor has also been added to perform the switch between the secure world and the normal world. Finally, we implemented a MicroTEE prototype on the Freescale i.MX6Q Sabre Lite development board and tested its performance. Evaluation results show that the performance of cryptographic operations in MicroTEE is better than it in Linux when the size of data is small.

cs.CR↗

arXiv2019

StegoAppDB: a Steganography Apps Forensics Image Database

In this paper, we present a new reference dataset simulating digital evidence for image steganography. Steganography detection is a digital image forensic topic that is relatively unknown in practical forensics, although stego app use in the wild is on the rise. This paper introduces the first database consisting of mobile phone photographs and stego images produced from mobile stego apps, including a rich set of side information, offering simulated digital evidence. StegoAppDB, a steganography apps forensics image database, contains over 810,000 innocent and stego images using a minimum of 10 different phone models from 24 distinct devices, with detailed provenanced data comprising a wide range of ISO and exposure settings, EXIF data, message information, embedding rates, etc. We develop a camera app, Cameraw, specifically for data acquisition, with multiple images per scene, saving simultaneously in both DNG and high-quality JPEG formats. Stego images are created from these original images using selected mobile stego apps through a careful process of reverse engineering. StegoAppDB contains cover-stego image pairs including for apps that resize the stego dimensions. We retainthe original devices and continue to enlarge the database, and encourage the image forensics community to use StegoAppDB. While designed for steganography, we discuss uses of this publicly available database to other digital image forensic topics.

eess.IV↗

arXiv2018

Unsupervised Trajectory Segmentation and Promoting of Multi-Modal Surgical Demonstrations

To improve the efficiency of surgical trajectory segmentation for robot learning in robot-assisted minimally invasive surgery, this paper presents a fast unsupervised method using video and kinematic data, followed by a promoting procedure to address the over-segmentation issue. Unsupervised deep learning network, stacking convolutional auto-encoder, is employed to extract more discriminative features from videos in an effective way. To further improve the accuracy of segmentation, on one hand, wavelet transform is used to filter out the noises existed in the features from video and kinematic data. On the other hand, the segmentation result is promoted by identifying the adjacent segments with no state transition based on the predefined similarity measurements. Extensive experiments on a public dataset JIGSAWS show that our method achieves much higher accuracy of segmentation than state-of-the-art methods in the shorter time.

cs.CV↗

arXiv2018

EviHunter: Identifying Digital Evidence in the Permanent Storage of Android Devices via Static Analysis

Crimes, both physical and cyber, increasingly involve smartphones due to their ubiquity. Therefore, digital evidence on smartphones plays an increasingly important role in crime investigations. Digital evidence could reside in the memory and permanent storage of a smartphone. While we have witnessed significant progresses on memory forensics recently, identifying evidence in the permanent storage is still an underdeveloped research area. Most existing studies on permanent-storage forensics rely on manual analysis or keyword-based scanning of the permanent storage. Manual analysis is costly, while keyword matching often misses the evidentiary data that do not have interesting keywords. In this work, we develop a tool called EviHunter to automatically identify evidentiary data in the permanent storage of an Android device. There could be thousands of files on the permanent storage of a smartphone. A basic question a forensic investigator often faces is which files could store evidentiary data. EviHunter aims to answer this question. Our intuition is that the evidentiary data were produced by apps; and an app's code has rich information about the types of data the app may write to a permanent storage and the files the data are written to. Therefore, EviHunter first pre-computes an App Evidence Database (AED) via static analysis of a large number of apps. The AED includes the types of evidentiary data and files that store them for each app. Then, EviHunter matches the files on a smartphone's permanent storage against the AED to identify the files that could store evidentiary data. We evaluate EviHunter on benchmark apps and 8,690 real-world apps. Our results show that EviHunter can precisely identify both the types of evidentiary data and the files that store them.

cs.CR↗