DHS Summer Research Team Research Workshop
August 3, 2022 at 1:00 PM – 4:00PM CST
The Critical Infrastructure Resilience Institute (CIRI) is hosting research teams as part of the DHS Summer Research Team (SRT) Program for Minority Serving Institutions. Three groups of faculty and students from three different universities are collaborating with CIRI researchers to conduct research projects throughout the summer, concluding with a virtual research workshop on August 3.
Please join us for part or all of the event!
Each faculty and student team will present for 30 minutes with 15 minutes of Q&A. We will break and reset between each group. The students will also record a poster presentation as part of our virtual CIRI SRT poster session — recording links and posters will be added to the CIRI website shortly before the workshop. More information on the virtual poster session to come soon.
1:00 p.m. – 1:45 p.m. Central Time
PASS+ Toward a Safe, Secure, and Resilient Cyber Environment
Research Team: PI Dr. Zhixiong Chen, Students Geordy Vincent and Ederson Mazariego, Mercy College
CIRI advisors: Professor Deming Chen, PhD
The PASS+ project is to build a trust cyber physical environment that can be used in scenarios that require identification, authentication and/or authorization without involving third parties. Its goal is to replace the current authentication principle of “ask and verify,” and “controlled” with “community proof of X”. The system developed is called Portfolio Artifact Service System (PASS+). Portfolio Artifacts (PAs) are building blocks to identify human beings as well as bots or apps. PAs for human subjects provide a holistic collection of bioinformation, characteristics, credentials, achievements, various stages of friends, and two way recommendation and references, while PAs for non-human subjects such as open source project software or bots provide unique identifiable features such as date of release, code base, developers, actuators, cryptographic hash value, digital signatures. PAs are controlled by their owners and creators and are stored in the decentralized blockchain. The service system is the infrastructure to create, store, retrieve, present PAs that are subject internal verification and external validation.
We have developed infrastructure on top of blockchain as a proof of concept in that we can create PAs that are digitally signed, approved, or assessed by the assessment engine, store these PAs in decentralized blocks, design and develop a platform that connects holders, certifiers or issuers, and end applications, design and develop smart contracts that could create and retrieve PAs. We also explore ontology to structure PAs systematically, develop JSON-LD schema to realize the PA structures, design a decision expert system to build an engine to assess competency. We also test the feasibility of pluggable “proof of X” consensus protocol into Ethereum open source code. Finally, we are investigating the performance if we control the generation of blocks from time, size and utility perspective, and understand the security implication on the security of the consensus protocol.
2:00 p.m. – 2:45 p.m. Central Time
Critical Infrastructure Security, Vulnerability and Threat Analysis for IoT-based Smart Power Grids and/or Cloud-based Healthcare Data Infrastructure
Research Team: PI Professor Sanjeev Kumar, Students Carlos Benevidas and Maxamilliano Garcia, University of Texas, Rio Grande Valley
CIRI advisor: Professor David Nicol, PhD, University of Illinois Urbana-Champaign
Critical Infrastructures are vulnerable to cyber-attacks that are making news frequently. For the summer research, our research group of two students worked on two different projects:
(i) Smart Grid infrastructure: We investigated the security resilience of Smart Electric Meters - Smart meters are being deployed rapidly in US and abroad, and 75% of US households now have smart electric meters deployed. Smart electric meter provides an important interface between the user, utility company, and the smart grid infrastructure. Our research team discovered that the smart meters are prone to security attacks, and the functioning of the smart meters can be compromised due to cyberattacks.
(ii) Healthcare infrastructure: Since Covid pandemic, there has been a rise in Telehealth and IoT based healthcare infrastructure for remote patient monitoring and medical services. Our team worked to design hardware circuits to generate healthcare EKG data in the lab to study the security vulnerabilities in the IoT based healthcare infrastructure for telemedicine that will be used to provide medical services to remote patients.
Research poster: Critical Security Vulnerabilities of Smart-Grid Power Meters
Video Presentation: Critical Security Vulnerabilities of Smart-Grid Power Meters (Garcia)
Research poster: Remote Healthcare Data Generation for Security Studies
Video Presentation: Remote Healthcare Data Generation for Security Studies (Benevidas)
3:00 p.m. – 3:45 p.m. Central Time
Development of the Big Bridge Data Across the Conterminous US for Deck Condition Rating and Prediction Using Machine Learning Algorithms
Research Team: PI Dr. Shenhua Huang; Student Fariba Fard; University of North Texas
CIRI advisor: Professor Eun Jeong Cha, PhD
This research proposes a novel approach for developing the spatiotemporal big bridge data (BBD) across the conterminous U.S. through employing Geospatial Information Science (GIS) and remote sensing techniques. The spatiotemporal BBD includes the potential predictor variables by contributing climate, and hazard data in addition to the National Bridge Inventory (NBI) and traffic data. This study collected the spatial BBD of 2020 which contains 612,388 bridges across the 48 adjoining states plus the District of Columbia. The performance evaluation of the classifiers on test sets indicates that random forest classifiers can successfully identify bridge decks and predict their condition ratings with ±1 buffer with 99.7% and 93.3% overall accuracy, respectively. The proposed approach can be employed to develop the spatiotemporal BBD by incorporating space-time bridge data, to develop different machine learning models, and to compare their performances.