Project Category: Internet of Things (IoT)

Collaborative Research: Scalable Penetration Test Generation for Automotive Systems

This project develops technology for systematic analysis of diverse safety, security, and reliability requirements in current and emergent vehicles. It enables early comprehension of conflict, trade-offs, and potential internal inconsistencies among the different requirements. The framework includes: (1) an adaptive virtual prototyping infrastructure that enables smooth integration of ECU, sensor, and actuator models; and (2) a concolic testing facility to generate penetration tests automatically for targeted adversary models. The analysis techniques developed in the research cross-cut hardware, software, and physical (sensory and actuarial) artifacts. The framework brings together currently disparate research in security, machine intelligence, and decision science. This project promises transformative technical and societal impacts through drastically improved safety, security, and reliability of diverse cyber-physical systems in general and automotive systems in particular. Research results will be integrated into graduate and undergraduate courses. A new workshop will be introduced to bring together experts in automotive safety, security, and reliability, and cross-cutting areas. Hands-on training modules for undergraduate and high school students will be developed using automotive simulator platforms. Participation of underrepresented students in the project will be actively encouraged. Industry connections will be used and actively pursued for technology transfer.

I/UCRC Phase I: Multi-functional Integrated System Technology (MIST)

The Multi-functional Integrated System Technology (MIST) I/UCRC is motivated by three major research/industry trends: 1.) Stepping beyond the current challenge of continued conventional scaling of integrated circuits (Moore?s Law); 2.) Exploring new functionalities at intersections of materials, processes, devices, and circuits for multi-functional systems; and 3.) Integrating nanoscale materials into micro/nanosystem manufacturing. Through industry-driven collaborative research initiatives, interdisciplinary mixing will be promoted at the MIST Center in order to catalyze innovation opportunities such as combinations of computing, sensing, actuation, and energy storage/generation on the same system on a chip (SoC) or system in package (SiP). The MIST Center intends to contribute to the definition of the roadmap to next-generation integrated electronic systems.

Dynamic Light Transport Acquisition and Applications to Computational Illumination

This research focuses on designing new light transport acquisition frameworks to capture dynamic scenes, characterization of dynamic light transport properties including sparsity and low-rank, and algorithms to exploit this information for computer vision applications. In particular, the project focuses on three main objectives. The first is design of an MEMs-based optical scanner coupled with high frame rate cameras to capture the full set of light transport paths at extremely fast timescales. The second contribution is new algorithms for adaptive light transport sampling using both physics-based and data-driven priors for light transport interpolation via generalized light transport flow. Finally, the project will provide applications of dynamic light transport for 3D scanning of deformable, moving, and specular objects. These innovations are evaluated in an integrated testbed via the optical scanner and the collection of a dataset of dynamic light transport for real-world scenes.

Resilient and Robust High Performance Computing Platforms for Scientific Computing Integrity

As technology advances, computer systems are subject to increasingly sophisticated cyber-attacks that compromise both their security and integrity. Recent research has highlighted that high performance computing platforms are vulnerable to these attacks. This situation is made worse by a lack of fundamental security solutions that both perform well and are effective at preventing threats. High performance computing platforms used in commercial and scientific applications involving sensitive, or even classified, data are frequently targeted by powerful adversaries. Current security solutions fail to address the threat landscape or ensure the integrity of sensitive data. As challenges grow in this area, both private and public sectors are expressing the need for robust technologies to protect computing infrastructure. Novel solutions hardening high performance computing platforms without loss of performance or energy efficiency are being developed by Dr. Jin and his research group. Advancing the state-of-the-art in high performance computing research, Dr. Jin is developing fine-grained memory protection that is scalable, adaptive, and lightweight to enhance intrusion detection and is addressing the threat landscape facing high performance computing environments. Dr. Jin’s work offers optimized, secure, and efficient solutions that will keep pace with security and user demands for both current and future platforms. Dr. Jin’s research helps the Department of Energy achieve its mission of providing secure exascale computing platforms to the scientific community.

Medium: Collaborative Research: Materials Authentication Using Nuclear Quadrupole Resonance Spectroscopy

The proposed authentication approach is based on comparing the Nuclear Quadrupole Resonance (NQR) spectra generated by the material under test with reference spectra stored in a secure database. The atoms of about half of all the elements in the periodic table contain so-called quadrupolar nuclei that generate NQR signals. This project will focus on the spectra of nitrogen, which is found in a large majority of pharmaceutical products. The NQR spectra are highly sensitive to chemical composition and physical properties, and thus act as unique “chemical fingerprints” that are difficult to emulate or falsify. Low power portable electronics and an integrated system resulting in instrumentation for noninvasive, nondestructive and quantitative testing will be developed for combining sensing, software, and data collection/analysis.

STARSS: Collaborative: IPTrust: A Comprehensive Framework for IP Integrity Validation

In this project, we develop a comprehensive and scalable framework for IP trust analysis and verification. We evaluate IPs of diverse types and forms and develop threat models, taxonomy and instances of IP trust/integrity issues. We investigate an integrative IP trust validation framework that combines the complementary abilities of functional, structural and parametric verification. We employ both statistical as well as judicious directed tests to sensitize rarely triggered malicious changes and observe their effects. The unified validation framework is flexible to detect diverse tampering efforts, scalable to large designs, and eliminates the need for a golden model. A platform for IP trust validation, threat analysis, and trust metrics would provide enabling technology to future designers to implement secure and trusted systems for diverse applications.

Planning Grant: Engineering Research Center for Intelligent Sensing, Mapping, and Forecasting of Water Quality for Sustainable Coastal Ecosystems (iCoast)

The iCoast planning grant will be used to bring together diverse experts from the domains of coastal ecology and restoration, watershed hydrology, public health, sensor and data science, artificial intelligence, and smart networked systems. The planning grant will pursue a well-defined set of activities, including a survey and two workshops. The state of Florida with its vast coastline and increasing coastal population is an appropriate location to perform this work and the proposing team includes researchers long focused on coastal ecology and water quality and well-connected to stakeholders. These experts will identify knowledge gaps, education and training needs, ways to broaden participation, and promising technology solutions for dependable, efficient, sustainable coastal water systems. It will leverage previous NSF and institutional investment in smart systems, coastal/estuarine science and engineering, and public health to modernize the workforce and produce long-term solutions to water quality problems. Continuous and effective chemical and microbrial monitoring of water quality in coastal areas as well as in upstream watersheds requires deep integration of basic science and engineering and convergence of knowledge from multiple disciplines, innovative experimental platforms and tools, and well-designed studies leading to new scientific understanding on complex relationships between humans the marine environment. While it poses tremendous challenges, it also illustrates the scale of opportunity for an ERC focused on coastal water quality that can address one of the most pressing problems in modern society.