Funded programs

1. Title: Distributed Space Adaptive Communications and Security for Multi-Constellation Networks
PI: Jonathan Black
Lead Institution: Virginia Tech
Co-PIs & Institution: Samantha Kenyon (VT)
Funding Program: Research FY23
FY23 Funds: $50,000

Summary: Rapid growth in the rate of commercial space launches and operations are fundamentally changing the economics of Space and presenting new opportunities for addressing our most urgent societal needs. New satellite-enabled telecommunications (satcom) companies, government programs, and remote learning and working requirements create the framework for successful public-private partnerships (PPPs) led by universities to tackle hard problems such as unconnected/under-connected regions and communities and security in new networking paradigms. This project will collaboratively research new space-based high-bandwidth networks to address the cybersecurity challenges of inter- and intra-constellation communications of internet satellite constellations.

2. Title: Cybersecurity Threat within Southwest Virginia’s Agriculture
PI: Karen Carter
Lead Institution: University of Virginia at Wise
Co-PIs & Institution: David Frazier (UVA-Wise)
Funding Program: Research FY23
FY23 Funds: $17,646

Summary: After years of traditional, generational farming methods, Southwest Virginia (SWVA) farmers are transitioning to smart technology utilization to assess and enhance value of their food production systems, in part as a possible avenue to meeting the global food demand. For the purpose of this study, smart technologies involve integrations of technology and data-driven agriculture applications to increase crop and herd production, along with increased quality. The proposed research is expected to gain insight in to SWVA agricultural producers’ awareness and utilization of smart technologies, as well as how to effectively determine training needs to assist in the access to services, personal information privacy (PIP), proprietary information safeguards, and IP address protection for SWVA producers.

3. Title: 5G-BEACON: Blockchain Enhanced Architecture and Coding for Orchestrated Networks
PI: Maice Costa
Lead Institution: Virginia Tech
Co-PIs & Institution: Yalin Sagduyu (VT), Sachin Shetty (ODU)
Funding Program: Research FY23
FY23 Funds: $30,000

Summary: 5G and beyond (nextG) systems will address the communication demands from numerous areas, such as entertainment, healthcare, manufacturing, agriculture, transportation, and public safety. In this project, we are particularly interested in their great potential to address the challenge of secure interoperability across networks for informed decisions. In the civilian domain, several applications can benefit from 5G for data-driven decision, including health monitoring systems, autonomous vehicles, and disaster response.

4. Title: Addressing the Workforce and Cybersecurity Challenges in Dairy Production
PI: Denis Gracanin
Lead Institution: Virginia Tech
Co-PIs & Institution: Gonzalo Ferreira (VT), Mohamed Azab (VMI), David Jones (VMI)
Funding Program: Research FY23
FY23 Funds: $35,000

Summary: One of the major issues in the modern dairy as well overall agricultural industry is the lack of awareness of cybersecurity issues, and their impact on the industry and the workforce involved. The main challenges facing the industry are mainly the lack of awareness about such issues, their critical nature, and the fact that they can be used to induce severe harm. The goal is to study the potential scenarios and issues that can lead to cyber attacks or can be induced by a cyber attacker.  The investigation will lead the development of a set of training scenarios that can be integrated into the final product that helps elevate the worker’s awareness about cyber security, its impact, and how to detect and handle attack situations. Virginia Military Institute will lead the effort related to analyzing the dairy farm equipment infrastructure, and the related smart products being used in the dairy farm based on the previous research in cybersecurity and Internet of Things (IoT).

5. Title: Securing Time Synchronization and Applications to 5G/Next‐G
PI: Tom Hou
Lead Institution: Virginia Tech
Funding Program: Research FY23
FY23 Funds: $75,000

Summary: The objective of this project is to identify Precision Time Protocol's (PTP) vulnerability to insider adversaries in the IoT scenario and validate this vulnerability through lab‐based hardware experiments. Once the
vulnerability is identified, we propose to develop a new byzantine‐resilient network time synchronization scheme, as an extension to the existing PTP. We will provide a rigorous proof on the correctness of our scheme and implement a proof‐of‐concept in our lab‐based testbed. More important, we will apply the new byzantine‐resilient network time synchronization scheme to our ongoing research on 5G URLLC in industry automation setting and 5G C‐RAN/ORAN.

6. Title: Security and Privacy Aware Testbed for Voice-based Social Networks
PI: Youna Jung
Lead Institution: Virginia Military Institute
Co-PIs & Institution: Mohamed Azab (VMI), Denis Gracanin (VT)
Funding Program: Research FY23
FY23 Funds: $50,000

Summary: The goals of this project are to explore the voice-based social networks (VBSNs) topics 1) Blockchain-based decentralized identity and reputation management for VBSNs, 2) Cloud-based voice recognition, voice modulation, and automatic translation between text and voice, 3) Open-source testbed of privacy-preserving VBSNs and to produce a testbed that allows researchers and developers to learn security and privacy issues of VBSNs and in turn evaluate and test their research outcomes or products. This project will significantly advance social networking systems and cybersecurity.

7. Title: Learning the Attackers’ Behavior for Defense of Smart Power Infrastructures
PI: Chen-Ching Liu
Lead Institution: Virginia Tech
Funding Program: Research FY23
FY23 Funds: $75,000

Summary: The electric power grid plays a pivotal role in modern life. From industrial work to domestic comfort, people depend on the power grid to provide electricity for varying purposes. In the wake of climate change and the accompanying quest to electrify transportation and heating, the electric power grid must take on an even more crucial role. Consequently, the cyber security, resilience, and controllability of the power grid is of paramount importance. The quest for a more reliable and resilient grid has led to the deployment of the smart grid, which is essentially a power grid enhanced with information and communications technology (ICT) to allow for remote control of the wide area grid. While this has made the smart grid a preferrable choice over the traditional grid, it has also made the grid vulnerable to cyberattacks that could lead to cascading failures, equipment damage, widespread and long-lasting outages, and adverse impacts on human lives and the economy. In this project, we will develop a novel decentralized technique for learning the attacker’s behavior, and hence, their motive in a cyberattack.

8. Title: Secure, distributed computing and communication
PI: Gretchen Matthews
Lead Institution: Virginia Tech
Funding Program: Research FY23
FY23 Funds: $99,984

Summary: This project centers on the use of coding theory to support security in two domains: distributed matrix multiplication and post-quantum cryptography. While the topics may seem disparate at first glance, there is commonality in the tools that we employ to address them. The first objective of this project is to design algorithms which allow a central node to provide a user the product of two matrices A and B which is obtained using N servers in such a way that no information about the matrices A and B is revealed even if T servers collude. The second objective of this project is to determine efficient decoding algorithms for the multivariate Goppa codes, which would provide an alternative to classic McEliece with a smaller public key size.

9. Title: 5G and 6G Security Through Deception
PI: Nishith Tripathi
Lead Institution: Virginia Tech
Co-PIs & Institution: Jeff Reed (VT)
Funding Program: Research FY23
FY21 Funds: $48,563

Summary: This project proposes a way to deal with cyber risks through deception rather than traditional encryption. Deception in the network can help protect a user's data and find and analyze an adversary. It can be applied at various points within the network. Deception has been used in Wi-Fi networks, an example being a "honey pot," which appears to be a vulnerable access point set up to monitor an intruder. A honey pot is a way to distract an intruder and learn more about the intruder. This project proposes to apply this defensive strategy to the 5G/NextG network.

10. Title: High Accuracy Automatic Code Repair for Mission-critical Software
PI: Danfeng (Daphne) Yao
Lead Institution: Virginia Tech
Co-PIs & Institution: Bimal Viswanath (VT), Ismini Lourentzou (VT)
Funding Program: Research FY23
FY23 Funds: $75,000

Summary:  Studies have shown that writing cryptographic code is error-prone, even for experts. Despite recent advances, state-of-the-art automatic code completion solutions have multiple deficiencies. Most data-driven code embedding solutions are not designed for addressing security-sensitive code. It is unclear how effective these approaches are in cryptographic code. In addition, there has not been any systematic investigation of various designs or comprehensive evaluation in terms of their security and accuracy capabilities. In this proposed project, our team will explore code embedding approaches and novel neural network designs for high accuracy code completion. Our solutions will assist software engineers with the development of security-critical codebases.

1. Title: Enhancing Cryptography Education Using Collaborative Visual Programming: A workforce development approach
PI: Sherif Abdelhamid
Lead Institution: Virginia Military Institute
Funding Program: Workforce FY23
FY23 Funds: $13,800

Summary: Cryptography is the science of securing sensitive information and ensuring that only the intended recipients can access and process the encrypted data. Internet shopping, online payments, and social networking websites have become increasingly popular with the advancement of the internet. However, hackers are getting more skilled than before to exploit existing vulnerabilities and attack these websites. Due to this, it has become increasingly important to introduce the science of cryptography to future generations, at a younger age, in straightforward and more engaging ways.  As a response, we are implementing a web-based programming learning tool called vizLab. The tool will help students bridge the gap between cryptography's mathematical foundations and computing by using a visual approach to programming. Students will learn to construct data encryption algorithms with minimal programming experience, using graphical icons representing the language’s essential elements. vizLab can execute the students’ block-based algorithms online; also, it can translate them into a high-level programming language (Python). Additionally, vizLab can store the completed blocks within an online cloud database. Students can share the constructed blocks with peers working on the same projects. Finally, students can integrate vizLab with learning management systems (LMS) to share their work with their instructors for assessment.

2. Title: Pathways for Cyberbiosecurity Workforce Preparation: Integrating Insights from Both Cybersecurity and Biosecurity
PI: Eric Kaufman
Lead Institution: Virginia Tech
Co-PIs & Institution: Feras Batarseh (VT), Anne Brown (VT), Susan Duncan (VT), Heather Lindberg (VWCC), B Bagby (VWCC)
Funding Program: Workforce FY23
FY23 Funds: $35,000

Summary: Cyberbiosecurity is an emerging field at the interface of the life sciences and the digital world and workforce development in cyberbiosecurity is a critical need in Southwest Virginia. Our specific aims for this project are: (1) crosswalk educational standards for biosecurity and cybersecurity into a comprehensive and integrative framework for cyberbiosecurity education, and (2) synthesize/analyze stakeholder perceptions that may guide curricular planning for cyberbiosecurity education.

3. Title: Women in Security
PI: Gretchen Matthews
Lead Institution: Virginia Tech

Funding Program: Workforce FY23
FY23 Funds: $25,000

Summary: Women in Security is based on the idea that mentoring has been identified as one of the key contributors to the success of women in STEM disciplines. A number of activities will take place through the year to build and strengthen the mentor relationships. The activities are designed to build community and create connections amongst those involved. They will expose the undergraduates to what research and graduate school are like in a supportive environment where mentors are encouraged to openly and honestly share their experiences. They will help students, undergraduates and graduate students, to see themselves in a cybersecurity career through increasing the swath of role models available to them. They will enhance the relationships of postdocs and early career faculty, broadening the scope of what they see as possible and equipping them with knowledge shared by those more senior in the field.

4. Title: Broadening Participation in Security
PI: Gretchen Matthews
Lead Institution: Virginia Tech
Funding Program: Workforce FY23
FY23 Funds: $38,000

Summary: This project will broaden the participation in cybersecurity education, research, workforce development, and innovation. It will connect people in related areas to enhance opportunities and build capacity, working to (1) identify target groups, (2) determine metrics for success, (3) create points of entry for engagement and participation, (4) generate increased participation in events and programming, and (5) strengthen relationships with stakeholders to broaden impact and reach. We aim to increase diversity in terms of gender, race, ethnicity, geographic origin throughout Southwest Virginia, socioeconomic background, levels of learning (middle school, high school, community college, 4-year intuitions), disciplines, thus expanding opportunities for research funding, furthering curriculum development, diversifying the node student body and ultimately the cybersecurity workforce.

5. Title: Cyber VIP: Use & Abuse of Personal Information
PI: Alan Michaels
Lead Institution: Virginia Tech
Funding Program: Workforce FY23
FY23 Funds: $77,444

Summary: The Use & Abuse of Personal Information (U&A) experiential learning effort engages a diverse multi-disciplinary group of undergraduate students to explore and quantify how personal infor-mation propagates across the Internet. The proposed CCI effort builds upon 2 years of experi-mentation that demonstrated the ability to generate realistic fake identities, perform one-time online interactions, and subsequently collect and analyze how that information is being both used and abused across email, SMS text, and voicemail modalities. Of particular interest are cross-site sharing behaviors (attributable due to one-time interaction), adherence to published privacy policies, trends across industries, root sources of spam / malicious content, and answering a variety of social science questions.

6. Title: Virginia Cybersecurity Education Conference Teacher Sponsorship
PI: David Raymond
Lead Institution: Virginia Tech
Funding Program: Workforce FY23
FY23 Funds: $4,560

Summary: The Virginia Cyber Range has built a strong user community in Virginia public high schools and colleges and we serve thousands of users in hundreds of Virginia schools each semester. Each year we host the Virginia Cybersecurity Education Conference, giving high school and college educators in the Commonwealth an opportunity to share ideas and continue to build the Virginia cybersecurity education ecosystem. This year, we are working with CCI partners in the Coastal node to host the conference on the campus of Old Dominion University. Many high school cybersecurity educators do not have travel funds available. In an effort to defray costs for high school teachers whose schools are unable to pay for their conference attendance, we would like to partner with CCI to fund their participation.

7. Title: Resilient and Secure BattleDrones: Drone Racing League for Southwestern Virginia
PI: Kevin Schroeder
Lead Institution: Virginia Tech
Co-PIs & Institution: Jonathan Black (VT), Mohamed Gebril (GMU)
Funding Program: Workforce FY23
FY23 Funds: $25,000

Summary: The work proposed here seeks to expand the CCI BattleDrones Competition beyond the inaugural year, expanding the number of institutions participating in the competition, offering the returning teams the opportunity to  incorporate lessons learned from the first competition while integrating new cyber-based obstacles and testing cyber resiliency techniques. The proposed project will therefore work with multiple members of the SWVA node, and will be scalable to other institutions around the Commonwealth. The second year of the competition will incorporate new challenges as each team will be responsible for designing the drone autonomy with the appropriate resiliency to endure various cyber obstacles such as intermittent GPS, spectrum denial, loss of camera visuals, signal spoofing, etc. Multiple successive races in a league format allows for developed drone technology, algorithms, and other existing cyber and cyber-physical tools to complement an autonomous drone system, demonstrating cyber effects and resiliency on public and private drone systems. Students will be encouraged to understand the existing infrastructure and to find ways and means to increase their chances of surviving and outperforming their opponents.

8. Title: Undergraduate Research Experience
PI: Prem Uppuluri
Lead Institution: Radford University
Funding Program: Workforce FY23
FY23 Funds: $7,600

Summary: This project will allow Radford University undergraduates to work on a CCI SWVA research projects at Virginia Tech. The students will be participating for eight hours a week for the fall semester of 2022 and the spring semester of 2023. They will be fully integrated into the project. Team members will be researching, proposing, and refining research questions answerable using the Open Source Intelligent (OSINT) collection engine. Questions may span any legal academic discipline, but will be evaluated on expected interest, feasibility of answering, and scientific approach by faculty experts.

1. Title: Managing Data and Resiliency for Autonomous Vehicles: Application in Search and Rescue
PI: Manish Bansal
Lead Institution: Virginia Tech

Funding Program: Seed Funding FY23
FY23 Funds: $20,000

Summary:  In the past years, simultaneous occurrence of pandemic and hurricanes presented significant challenges to FEMA and therefore, FEMA is turning to robotic automation for variety of its activities. Though autonomous ground vehicle embedded with robotic cameras (AGV-RC) can augment the performance and safety of US&R task forces, the inadequate real-time data management are hindering the reliance on this technology. Specifically, a robotic cameras system requires huge amount of data processing and storage capacity to make real-time decisions for dynamic search operations, even though there is a possibility of redundant information provided by the cameras. The overarching objective of this research project is to develop novel mathematical optimization models and algorithms for solving stochastic combinatorial optimization problems that arise in planning teleoperations for AGV-RC.

2. Title: Federated and Secure Spectrum Learning for NextG Communications Systems
PI: Tugba Erbek
Lead Institution: Virginia Tech
Co-PIs & Institution: Yalin Sagduyu (VT), Kai Zeng (GMU)

Funding Program: Seed Funding FY23
FY23 Funds: $20,000

Summary:  As Deep Learning (DL) becomes a core part of next generation (NextG) systems, there is an increasing concern about the vulnerability of DL to adversarial effects. In this context, smart adversaries may tamper with the training and/or test inputs to DL algorithms embedded in NextG communications. The problem of learning in the presence of adversaries is the subject to the study of adversarial machine learning (AML) that has received increasing attention in computer vision and NLP domains. The first research thrust in this project is to investigate the emerging attack surface of AML for Federated (FL) and corresponding attacks and defense schemes. Specifically, we will focus on the free‐riding attacks where some clients do not contribute to the FL model updates while still receiving the global model from the server. We will pursue developing a game‐theoretic framework to quantify the interactions among free‐riding and participating clients. We will start with establishing the non‐cooperative Nash equilibrium strategies and then extend the analysis to coalition games through cooperative game theory.

3. Title: New Cryptographic Audit Tools for Effective Data Integrity Attestation in Large-scale Storage-as-a-service Infrastructure
PI: Thang Hoang
Lead Institution: Virginia Tech
Funding Program: Seed Funding FY23
FY23 Funds: $20,000

Summary: The overarching objecŒve of this proposal is to develop a series of new cryptographic data audit protocols for effectiŒve informatiŒon security assurance, which not only addresses emerging soundness concerns of data outsourcing raised by the client but also achieves saŒsfactory eƒfficiency (e.g., low computaŒon cost, opŒmal audit tag size) for the service provider to comply with standard data regulaŒons. The three key research thrusts are 1) Eƒcient cryptographic audit protocols for large-scale data integrity check, 2) Enable public auditability to improve transparency and further use-cases and 3) Formal security analysis, implementaŒon, and experiments.

4. Title: Enhancing Cybersecurity of Power Systems with Reinforcement Learning
PI: Ming Jin
Lead Institution: Virginia Tech
Co-PIs & Institution: Peter Boling (VT)

Funding Program: Seed Funding FY23
FY23 Funds: $20,000

Summary: The project goals are to design, develop and validate algorithms for the detection of cyberattacks at Information and Communication Technology (ICT). By synergizing the optimization-based attack modeling with the inverse reinforcement learning (IRL) framework, we will develop a multi-stage detection method that continuously monitor ICT network data to capture complex sequential anomalies with interpretable severity metrics to inform proper mitigation measures.

5. Title: Quantum Computations for Smart Electric Grids: Enhancing Situational Awareness and Securing Power Systems Operations
PI: Vasileios Kekatos
Lead Institution: Virginia Tech
Co-PIs & Institution: Jamie Sikora (VT)
Funding Program: Seed Funding FY23
FY23 Funds: $20,000

Summary: We propose to develop, analyze, and evaluate quantum and quantum-classical algo­ rithms for enhancing cyberphysical security and efficiency of electric power systems. The novel algorithms aim to: i) expedite monitoring functionalities (power system state estimation (PSSE), bad data processing, anomaly detection); and ii) scale up optimal power flow (OPF), a challenging yet omnipresent optimization task, to power networks having thousands of nodes.

6. Title: Secure & Privacy-Preserving Deep-Learning Framework for Smart and Connected Communities
PI: Angelos Stavrou
Lead Institution: Virginia Tech
Co-PIs & Institution: Xinghua Gao (VT), Thinh Doan (VT)
Funding Program: Seed Funding FY23
FY23 Funds: $20,000

Summary: Smart and sensor-enabled buildings have emerged as the next generation of smart interfaces for humans to interact with the environment, technologies, and each other. People are spending 87% of their time inside buildings for work, rest, and entertainment. Currently, building technologies are evolving towards the integration of physical, digital, and human systems in the building environment to deliver a sustainable, prosperous, and inclusive future for people. There are, however, some major challenges in this domain primarily pertaining to the security, privacy, and use of the human and building generated information. Our interdisciplinary research team attempts to address these challenges by introducing a secure and privacy preserving data collection and sharing platform aimed at indoor sensing data for smart and connected residential communities. Using this platform, we aim to foster the development of novel deep-learning data analysis approaches that would balance multiple sustainability requirements for modern residential communities leveraging building sensors and actuators.

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Funding Program: Quantum FY23
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