skip to main content


Title: Hardware Secure Execution and Simulation Model Correlation using IFT on RISC-V
With Heterogeneous architectures and IoT devices connecting to billions of devices in the network, securing the application and tracking the data flow from different untrusted communication channels during run time and protecting the return address is an essential aspect of system integrity. In this work, we propose a correlated hardware and software-based information flow tracking mechanism to track the data using tagged logic. This scheme leverages the open-source benefits of RISC V by extending the architecture with security policies providing precise coarse grain management along with a simulation model with minimal overhead.  more » « less
Award ID(s):
1814420
NSF-PAR ID:
10286474
Author(s) / Creator(s):
; ;
Date Published:
Journal Name:
ACM Great Lakes Symposium on VLSI (GLSVLSI)
Page Range / eLocation ID:
409 to 414
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. J. Y. C., Chen (Ed.)
    Controlling and standardizing experiments is imperative for quantitative research methods. With the increase in the availability and quantity of low-cost eye-tracking devices, gaze data are considered as an important user input for quantitative analysis in many social science research areas, especially incorporating with virtual reality (VR) and augmented reality (AR) technologies. This poses new challenges in providing a default interface for gaze data in a common method. This paper propose GazeXR, which focuses on designing a general eye-tracking system interfacing two eye-tracking devices and creating a hardware independent virtual environment. We apply GazeXR to the in-class teaching experience analysis use case using external eye-tracking hardware to collect the gaze data for the gaze track analysis. 
    more » « less
  2. Internet of Things (IoT) frameworks are designed to facilitate provisioning and secure operation of IoT devices. A typical IoT framework consists of various software layers and components including third-party libraries, communication protocol stacks, the Hardware Abstraction Layer (HAL), the kernel, and the apps. IoT frameworks have implicit data flows in addition to explicit data flows due to their event-driven nature. In this paper, we present a static taint tracking framework, IFLOW, that facilitates the security analysis of system code by enabling specification of data-flow queries that can refer to a variety of software entities. We have formulated various security relevant data-flow queries and solved them using IFLOW to analyze the security of several popular IoT frameworks: Amazon FreeRTOS SDK, SmartThings SDK, and Google IoT SDK. Our results show that IFLOW can both detect real bugs and localize security analysis to the relevant components of IoT frameworks. 
    more » « less
  3. The objective of this research is to compare the effectiveness of different tracking devices underwater. There have been few works in aquatic virtual reality (VR) - i.e., VR systems that can be used in a real underwater environment. Moreover, the works that have been done have noted limitations on tracking accuracy. Our initial test results suggest that inertial measurement units work well underwater for orientation tracking but a different approach is needed for position tracking. Towards this goal, we have waterproofed and evaluated several consumer tracking systems intended for gaming to determine the most effective approaches. First, we informally tested infrared systems and fiducial marker based systems, which demonstrated significant limitations of optical approaches. Next, we quantitatively compared inertial measurement units (IMU) and a magnetic tracking system both above water (as a baseline) and underwater. By comparing the devices rotation data, we have discovered that the magnetic tracking system implemented by the Razer Hydra is more accurate underwater as compared to a phone-based IMU. This suggests that magnetic tracking systems should be further explored for underwater VR applications. 
    more » « less
  4. Camera tracking is an essential building block in a myriad of HCI applications. For example, commercial VR devices are equipped with dedicated hardware, such as laser-emitting beacon stations, to enable accurate tracking of VR headsets. However, this hardware remains costly. On the other hand, low-cost solutions such as IMU sensors and visual markers exist, but they suffer from large tracking errors. In this work, we bring high accuracy and low cost together to present MoiréBoard, a new 3-DOF camera position tracking method that leverages a seemingly irrelevant visual phenomenon, the moiré effect. Based on a systematic analysis of the moiré effect under camera projection, MoiréBoard requires no power nor camera calibration. It can be easily made at a low cost (e.g., through 3D printing), ready to use with any stock mobile devices with a camera. Its tracking algorithm is computationally efficient, able to run at a high frame rate. Although it is simple to implement, it tracks devices at high accuracy, comparable to the state-of-the-art commercial VR tracking systems. 
    more » « less
  5. null (Ed.)
    Internet of Things (IoT) devices are becoming increasingly popular and offer a wide range of services and functionality to their users. However, there are significant privacy and security risks associated with these devices. IoT devices can infringe users' privacy by ex-filtrating their private information to third parties, often without their knowledge. In this work we investigate the possibility to identify IoT devices and their location in an Internet Service Provider's network. By analyzing data from a large Internet Service Provider (ISP), we show that it is possible to recognize specific IoT devices, their vendors, and sometimes even their specific model, and to infer their location in the network. This is possible even with sparsely sampled flow data that are often the only datasets readily available at an ISP. We evaluate our proposed methodology to infer IoT devices at subscriber lines of a large ISP. Given ground truth information on IoT devices location and models, we were able to detect more than 77% of the studied IoT devices from sampled flow data in the wild. 
    more » « less