Funded programs

1. Title: Weighted Graph States for Quantum Network Applications
PI: Ed Barnes
Lead Institution: Virginia Tech
Co-PIs & Institution: None
Funding Program: FY24 Research Program

Summary: This project will explore the usefulness of weighted graph states for quantum networks. This will be done by pursuing two main lines of research: (i) Determine to what extent weighted graph states are useful in their own right for quantum networking protocols, and (ii) develop a systematic method to produce nearly perfect graph states by preparing weighted graph states and measuring a subset of the qubits. The PI has already provided evidence that the latter is possible in a recent work that showed that nearly perfect GHZ states can be produced by preparing linear weighted graph states of 2n + 1 photons and measuring n of them in an appropriately chosen basis (see Fig. 1). However, it remains unknown whether more general (nearly) perfect graph states can be extracted from larger weighted graph states in a similar fashion and, if so, what weighted graph states should be used for this purpose. 

2. Title: Cybersecurity Threats in a Federated Supply chain Architecture
PI: Zachary Bowden
Lead Institution: Virginia Tech
Co-PIs & Institution: Kevin Kefauver (VT)
Funding Program: FY24 Research Program

Summary: This project entails a comprehensive, macro-level cybersecurity analysis of an existing federated data exchange network (FENIX, the European Federated Network of Information eXchange in LogistiX) to uncover threats and risks across the entire supply chain network and illuminate how they affect different nodes and data integrity. The research will address a recognized cybersecurity gap in supply chains, especially as they evolve through the adoption of advanced technologies, while also informing data sharing decisions for critical players in the supply chain. This project builds on existing efforts of the Virginia Tech-led Dock-to-Door Coalition and will facilitate future funding for additional research to address cybersecurity concerns associated with supply chain data exchange.

3. Title: Using Intelligvent Converstational Agents to Empower Adolescents to be Reslilient Against Cybergrooming
PI: Jin-Hee Cho
Lead Institution: Virginia Tech
Co-PIs & Institution: Lifu Huang, San Won Lee (VT)
Funding Program: FY24 Research Program

Summary: This research will aim to fill this gap by developing a chatbot-based experiential learning intervention program that raises adolescents’ knowledge and awareness about risk factors for cyber grooming and increases self-efficacy to protect themselves from cyber grooming and cope with risky situations. To achieve this, we will develop a chatbot-based intervention program called RY-LAI, representing Resilient Youth Learn through Artificial Intelligence (pronounced as real AI).

4. Title: Timely Communication under Adversarial Attacks
PI: Maice Costa
Lead Institution: Virginia Tech
Co-PIs & Institution: Yalin Sagduyu (VT)
Funding Program: FY24 Research Program

Summary: With this project, we aim to contribute to advancing the state-of-the-art in the communication layer of cyber-physical systems, with an innovative combination of semantic communication and distributed learning. In many cases, the communication should deliver time-sensitive and private information on time, possibly in a hostile environment.  Hence, we will consider a framework developed to operate under adversarial effects, to analyze important trade-offs involving reliability and timeliness under the presence of an adversary that may eavesdrop and jam the wireless channel and compromise CPS secure operation. In many cases, it is desirable that communication can go undetected, so we also consider the conditions for covertness and stealth. 

5. Title: Fundamentals of Privacy-Detectability-Timeliness Tradeoff in Wireless Networks
PI: Harpreet Dhillon
Lead Institution: Virginia Tech
Co-PIs & Institution: None 
Funding Program: FY24 Research Program

Summary: Even though AoI has received significant attention over the past decade, the PI has identified two fundamental open problems that relate to the tradeoffs of AoI with privacy and detectability. As will be discussed shortly, these tradeoffs have important implications for the design of critical defense infrastructure, such as radar networks, and the conceptualization of emerging commercial applications, such as Metaverse.  The objective of this project is to obtain preliminary results for the two tradeoffs discussed above so that we can go for large external grant proposals as discussed in the sequel.

6. Title: Privacy-Aware Federated Learning in Heterogeneous IoT
PI: Thang Hoang
Lead Institution: Virginia Tech
Co-PIs & Institution: Tran Viet Xuan Phuong (ODU)
Funding Program: FY24 Research Program

Summary: The overarching objective of this proposal is to design, analyze and implement innovative privacy-aware FL functionalities that enable the computation heterogeneity of real IoT ecosystem thereby, enhancing the its security and performance in overall. We specifically focus on the core secure aggregation functionality, in which we aim to develop new techniques that can securely aggregate local AI models of different sizes and complexity with privacy and integrity guarantees even in the presence of powerful adversaries (e.g., corrupted aggregator, malicious workers).

7. Title: Securing Large Language Models for Enhanced Supply Chain Cybersecurity
PI: Ming Jin
Lead Institution: Virginia Tech
Co-PIs & Institution: Peter Beling (VT)
Funding Program: FY24 Research Program

Summary: Our project aims to delve into the intricacies of the LLM's latent space, identifying and addressing vulnerabilities specific to supply chain interfaces. By applying novel methodologies like Latent Space Projections and Transfer Learning Adjustments, we intend to enhance the robustness of supply chain operations. Additionally, the integration of innovative reinforcement learning techniques fortifies the supply chain against potential cyber threats, ensuring harmonized and secure operations.

8. Title: Identifying Human Factors Related Cybersecurity Risks and Vulnerabilities in Driving Automation Systems equipped CMV Fleets
PI: Xiaojian Jin
Lead Institution: Virginia Tech
Co-PIs & Institution: Zeb Bowden, Andrew Krum, Rich Hanowski (VTTI)
Funding Program: FY24 Research Program

Summary: The goal of this project is to investigate the cybersecurity risks and vulnerabilities associated with commercial motor vehicle (CMV) fleets equipped with driving automation systems, including automated driving systems (ADS) and advanced driver assistance systems (ADAS), to develop an understanding of the human factors and behaviors contributing to these risks and vulnerabilities.

9. Title: Quantum Algorithms for Ideal Class Group Computations
PI: Jason LeGrow
Lead Institution: Virginia Tech
Co-PIs & Institution: Travis Morrison, Jamie Sikora (VT)
Funding Program: FY24 Research Program

Summary: In this project we will study quantum algorithms for computing the structure of the ideal class group in the context of cryptography. Our results will yield faster, simpler, and more secure quantum algorithms for ideal class group structure computation, enabling post-post-quantum and quantum-assisted advanced functionalities—such as exotic signatures and trapdoor verifiable delay functions—from modern number theoretic primitives sooner than would be possible using existing algorithms.

10. Title: Digital Twins for Cyber Resilience of a Low Carbon Power and Energy Infrastructure
PI: Chen-Ching Liu
Lead Institution: Virginia Tech
Co-PIs & Institution: None
Funding Program: FY24 Research Program

Summary: By incorporating the insights from our prior work and leveraging the digital twin of the smart grid, our proposed method seeks to develop a robust anomaly detection system. This Machine Learning (ML) approach will enable the identification of abnormal behaviors and potential cyber threats in real-time, thereby enhancing the overall cybersecurity of the distribution system, using the VTES grid model available at the Power and Energy Center as a real-world study case. The combination of digital twins and advanced anomaly detection techniques is critical for proactively mitigating cyber risks and ensuring the secure operation of critical infrastructure systems.

11. Title: Software Defined Radio and O-RAN for Mobile Distributed MIMO (dMIMO)
PI: Lingjia Liu
Lead Institution: Virginia Tech
Co-PIs & Institution: none
Funding Program: FY24 Research Program

Summary:
Massive MIMO is an enabling technology for 3GPP 5G NR and is envisioned as a key one for Beyond 5G networks (e.g., 5G-Advanced, 6G, etc). To circumvent the form factor limitation of gNBs and to ensure a more unified network performance, distributed MIMO (dMIMO) operations such as distributed antenna systems (DAS), dynamic point selection (DPS), coordinated multi-point (CoMP) (a.k.a. network MIMO), and multiple transmit/receive point (multi-TRP) have been introduced in various releases of 3GPP. In the “Mobile Distributed MIMO: Learning Meets Spreading in Networking” project funded by OUSD(R&E)’s FutureG Program, we will be mainly focusing on system design and hardware prototype for mobile dMIMO systems. In this CCI project, we will be focusing on the software defined radio (SDR) prototype of dMIMO as well as the O-RAN system design and prototype for mobile dMIMO systems.

12. Title: Building a High-performance Intrusion Detection System for Virginia Tech's IPv4 and IPv6 Networks
PI: Wenjing Lou
Lead Institution: Virginia Tech
Co-PIs & Institution: Wenjing Lou (VT)
Funding Program: FY24 Research Program

Summary: This proposal outlines a research plan with the following objectives and intellectual merits:
• We will employ representation learning to enhance the IDS performance by learning new network traffic representations. This approach aims to improve the IDS’s ability to differentiate anomalies from benign samples. In this project, we will develop a contrastive learning-based IDS expected to significantly boost performance.
• In collaboration with Virginia Tech’s IT Security lab, our project aims to develop, evaluate, and deploy the proposed IDS systems specifically tailored for the Virginia Tech campus network. We will focus on IDS’s functionality in a dual-stack environment, where both IPv4 and IPv6 coexist. We will specifically examine the transferability of IDS models built for IPv4 traffic to effectively handle and protect against threats in IPv6 traffic.
• We also plan to create a well-curated NIDS dataset that can be used by the broader community.

13. Title: Coding theory for security and privacy
PI: Gretchen Matthews
Lead Institution: Virginia Tech
Co-PIs & Institution:  None
Funding Program: FY24 Research Program

Summary: In this proposal, we consider techniques to train machine learning models on data held by individual entities while preserving the privacy of the data. We consider the standard setting in which the holders are honest but curious, meaning they accurately share information related to the data they hold (though not the data itself) and are willing to work with others to gain access. We also consider settings in which adversarial nodes which report false information related to the data they hold. Encoding the individual datasets and distributing the compu- tations across nodes can address this issue, but a number of questions remain about how to best do that even in the standard setting. 

14. Title: Graduate Research & Innovation Design: Secure Connectivity for Renewable Devices in the grid (GRID-SECURED)
PI: Ali Mehrizi-Sani
Lead Institution: Virginia Tech
Co-PIs & Institution: none
Funding Program: FY24 Research Program

Summary: The electric energy delivery system, commonly known as the power grid, is a critical infrastructure and a component of the national security.  Transition to renewable energy resources, which can be deployed in a distributed manner close to power consumption sites, has necessitated the availability of communication infrastructure. Such infrastructure additionally has the potential to enhance the performance of control and protection facilities in the power grid. However, this also makes the power grid an attractive target for cyber-attacks. The overarching objective of this project is to create and demonstrate the GRID-SECURED framework. GRID-SECURED is a scalable, distributed, and cost-effective technology to enable secure and resilient communication to control and monitor distributed energy resources (DER), including grid-forming (GFM) inverters, within the power system and/or microgrids.

15. Title: An Empirical Evaluation of Large Language Models (LLMs) in Generating Security Tests to Mitigate Supply Chain Attacks
PI: Na Meng
Lead Institution: Virginia Tech
Co-PIs & Institution: Daphne Yao (VT)
Funding Program: FY24 Research Program

Summary: In this project, we will leverage the state-of-the-art AI-powered language model— ChatGPT— to automatically generate security test cases, and to demonstrate how vulnerable library dependencies facilitate the supply chain attacks to given software applications. By running such test cases during software development and maintenance, developers can learn the necessary cybersecurity knowledge of supply chain attacks, become more serious when getting suggestions from automatic vulnerability detectors as well as fixers, and thus create secure by design as well as secure by default software.

16. Title: Use & Abuse of Personal Information
PI: Alan Michaels
Lead Institution: Virginia Tech
Co-PIs & Institution:  Madison Boswell (VT)
Funding Program: FY24 Research Program

Summary:  Since 2020, Virginia Tech National Security Institute (VTNSI) has run a multi-disciplinary Vertically Integrated Project called Use & Abuse (U&A) of Personal Information (PI). U&A is an experiential learning project aligning with all three of CCI’s mission objectives, exploring how PI propagates across the Internet. Over the last academic year, a team of 58 students from 15 academic majors at Virginia Tech and Radford University developed an innovative database of 100,000 realistic fake identities that conform to the U.S. population [1]. During the upcoming year, we will disseminate these fake IDs, which stand up to reasonable scrutiny, with online second parties at scale using one-time transactions during a partially-automated signup event in Fall 2023.

17. Title: Robust Classification of Adversarial Images from Generative AI Models 
PI: Bimal Viswanath
Lead Institution: Virginia Tech
Co-PIs & Institution: Taejoong Chung, Peng Gao (VT)
Funding Program: FY24 Research Program

Summary: In this project, we focus on the capability of generative models to produce convincing synthetic images. This capability can be easily misused today, raising several security threats. Therefore, we will develop new tools and methods to systematically investigate the effectiveness of existing defenses against this rapidly evolving threat and propose new directions for robustly detecting synthetic images.

18. Title: Real-time Integrated Misinformation Campaign Alarm and Tracking System in the Age of AI
PI: Yaling Yang 
Lead Institution: Virginia Tech
Co-PIs & Institution: Lingjia Liu, Yang Yi (VT)
Funding Program: FY24 Research Program

Summary: The objective of this project is to conduct a comprehensive analysis of the most recent cases of misinformation campaigns, examining their tactics in exploiting diverse online platforms, and ultimately seeking effective solutions to mitigate their substantial threat to our society. By delving into these cases, we aim to gain a deeper understanding of the strategies employed by these campaigns, their impact on public discourse, and the extent of their societal implications. Through this research, we endeavor to develop robust countermeasures to combat the detrimental effects of misinformation and safeguard the integrity of our information ecosystem.

19. Title: Understanding and Protecting the Privacy for Health Data Sharing and Analysis in Virginia 
PI: Hailong Zhang
Lead Institution: Virginia Tech
Co-PIs & Institution: 
Funding Program: FY24 Research Program

Summary: The overarching goal of this project is to assess the practical impacts of privacy regulations and technologies, and to develop new PETs for health data. Our specific objectives are:
-Delineate the social, societal, political, and health impacts (e.g., disparities) of legislative and technological protections on sharing and analysis of health data.
-Enhance the quantitative understanding of the practicality and efficacy of existing PETs in various applications of health data analysis.
-Develop a robust framework and solution tailored for a multi-source environment, wherein different sets of health data demand varying degrees of privacy protection.

1. Title: (SGDT) Smart-built Gamified environment as an autonomous collaborative cybersecurity Digital-Twin facilitating user-behavior modeling in the presence of attacks
PI: Mohamed Azab
Lead Institution: Virginia Military Institute
Co-PIs & Institution: Denis Gracanin, Stephanie Travis (VT)
Funding Program: FY24 Workforce 

Summary: In this proposal, we present a cybersecurity gamified digital-twin experimentation and exercise platform emulating large-scale distributed smart-built environments for user behavior modeling and investigation in attack and defense situations. We build on the existing collaboration between two Senior Military Colleges, Virginia Military Institute (VMI) and Virginia Tech (VT). The VMI and VT PIs have collaborated since 2019 on related research problems. VMI cadets and VT graduate/undergraduate students work together to support the PIs’ research activities. High school students selected from Montgomery and/or Rockbridge counties will be recruited to work on the project. VT PI Gracanin is also an Adjunct Professor at the VMI Department of Computer and Information Sciences where he teaches capstone courses (CIS 480 and CIS 490). VT PI is also a member of the VMI CyDef Lab team. VT Co-PI Travis is at the VT National Security Institute (NSI) where VT PI Gracanin is an affiliate faculty. VMI PI is a member of graduate students committees at VT and a collaborator with the VT DVE lab, and NSI at VT. VMI PI is also a member of the VMI CyDef Lab team.

2. Title: The Cybersecurity, Privacy, and Ethics of Electroencephalography
PI: Aaron Brantly
Lead Institution: Virginia Tech
Co-PIs & Institution: Nataliya Brantly (VT)
Funding Program: FY24 Workforce 

Summary: The proposed project aims to build an EEG headset capable of translating brainwaves into recognizable speech patterns. By constructing an EEG device using 3D printing and programming software, the practicality of brain-to-speech conversion and preliminary insights into potential privacy concerns regarding a person’s thoughts can be assessed. An evaluation of the capabilities and limitations of real-time EEG speech decoding will provide a foundation for ethical considerations surrounding advancements in neurotechnology and its use commercially and in medicine.

3. Title: Understanding How Software Developers Secure User Interfaces in Rapid Release Environments
PI: Chris Brown
Lead Institution: Virginia Tech
Co-PIs & Institution: none
Funding Program: FY24 Workforce 

Summary: The goal of the proposed work is to understand current practices and challenges for securing user interfaces in modern software. In particular, we will explore the following research question: How do software developers secure user interfaces in rapid release environments? To answer this question, we will conduct a series of research activities to investigate GUI testing for security in CI/CD projects and the challenges therein.

4. Title: Moving Target Defense for Time-Sensitive Cyber-Physical Systems (CPS)
PI: Thidapat Chantem
Lead Institution: Virginia Tech
Co-PIs & Institution: Mohamed Azab (VMI)
Funding Program: FY24 Workforce 

Summary: In this project, we will use moving target defense (MTD) to secure RT-CPS. Specifically, we will implement MTD through hardware redundancy and configuration diversity. This will permit cross-layer awareness and strategic guidance to fortify system security against evolving threats. By incorporating hardware redundancy, the approach enables an RT-CPS to maintain operational integrity and meet deadline requirements even when specific components are compromised, while configuration diversity ensures that the system’s setup varies over time or in response to detected threats, making it more difficult for attackers to exploit known vulnerabilities. Cross-layer awareness is critical in this paradigm, as it allows for a comprehensive understanding of the system’s state across both its physical and cyber dimensions, enabling more effective and adaptive defense strategies. Strategic guidance, in this context, involves making informed decisions about when and how to alter hardware configurations and deploy redundancy to best protect the system. This multi-faceted strategy enhances the resilience of CPS against sophisticated cyber-attacks, ensuring their reliability and safety in critical applications.

5. Title: FY24 CCI SWVA and NextUp Solutions Internships
PI: Steve Cooper
Lead Institution: Virginia Tech
Co-PIs & Institution: none
Funding Program: FY24 Workforce 

Summary: Next Up Solutions provided 6 internships working with a local county government.  The students performed a variety of cybersecurity tasks including: 1)  Assist in conducting vulnerability assessments and penetration testing to identify potential security weaknesses in our systems and networks, 2) Support the implementation and maintenance of security controls, policies, and procedures, 3) Participate in the monitoring and analysis of security events and incidents, including log analysis and threat intelligence research, 4) Assist in the development and execution of security awareness training programs, 5) Collaborate with cross-functional teams to identify and mitigate security risks across different departments and projects.

6. Title: Development of Quantum Information Theory-based Solutions for Counter UAS
PI: Vsevolod Ivanov
Lead Institution: Virginia Tech
Co-PIs & Institution: none
Funding Program: FY24 Workforce 

Summary: The PI seeks support to accelerate the growth of relationships between Corvus Labs and the Virginia Tech National Security Institute with the goal of developing a research and funding portfolio in the area of Quantum Sensing, a destination area investment for NSI. Corvus Labs is based in Blacksburg Virginia, and is a sensor fusion technology company focused on developing autonomous systems for defense and industrial use. These systems are underpinned by a strong portfolio of artificial intelligence models for real-time detection and tracking of aerial, maritime, and ground-based objects, and a physics-first approach to synthetic data generation for rapid and robust model training.

7. Title: Federated Edge Intelligence with Multi-modal Learning
PI: Bo Ji 
Lead Institution: Virginia Tech
Co-PIs & Institution: Jin Yi Yoon (VT)
Funding Program: FY24 Workforce 

Summary: Edge intelligence relies heavily on the sheer volume of data, which often presents two primary challenges: 1) multi-modal data and 2) the need to process such data near the users. To that end, this project is focused on achieving edge intelligence with multi-modal learning. We will employ federated learning to overcome resource constraints and limited data availability at the individual user level, integrating diverse knowledge across users to enhance the performance of edge artificial intelligence (AI) systems, while preserving data privacy.

8. Title: Enabling On-Device Live Captioning in Mixed Reality Systems
PI: Bo Ji
Lead Institution: Virginia Tech
Co-PIs & Institution: Siwei Cao (VT)
Funding Program: FY24 Workforce 

Summary: In recent years, Mixed Reality (MR) technologies have surged in popularity, blending the physical world with digital elements to create environments where physical and virtual objects coexist, enabling real-time interaction [1]. However, there’s significant room for improving the MR ecosystem, particularly in enhancing user experience and accessibility features.  Live captioning, a highly desired feature not yet implemented, offers real-time transcription of spoken content, greatly enhancing communication accessibility for hearing-impaired users, linguistic learning support for non-native speakers, and comprehension for users irrespective of language proficiency or hearing ability.

9. Title: OSINT and Generative AI for Cyber Vulnerability Assessment
PI: Kurt Luther
Lead Institution: Virginia Tech
Co-PIs & Institution: none
Funding Program: FY24 Workforce 

Summary: Many small businesses are unaware of the cyber risks they face or how to mitigate them.  To address this issue, starting in Fall 2023, the PI recruited and trained a team of students to conduct free cyber vulnerability assessments for Virginia small businesses. Vulnerability assessments can have many components, including internal network discovery and vulnerability scanning with tools like Nmap and Nessus; security controls interviews based on frameworks like CIS and LESS; and physical security reviews looking for access control, fire suppression, and water detection. Our team’s focus is Open Source Intelligence (OSINT), a form of digital investigation using only publicly available data. OSINT techniques are widely used for many types of investigations in domains ranging from cybersecurity (e.g., reconnaissance for pentesting, cyber threat intelligence) to journalism to human rights. For vulnerability assessments specifically, OSINT uses include attack surface mapping (identifying all publicly-accessible digital assets such as website, servers, webcams, and social media accounts, as well as their vulnerabilities); brand monitoring (examining review sites, social media, and forum posts for mentions of the businesses, including negative rumors and disinformation campaigns); and breach data discovery (finding leaked credentials on the Dark Web or other sources).

10. Title: Cybersecurity Competition Capacity Building: CTF Author Interns
PI: David Raymond
Lead Institution: Virginia Tech
Co-PIs & Institution: none
Funding Program: FY24 Workforce 

Summary: The Virginia Cyber Range infrastructure includes an integrated cybersecurity capture the-flag competition platform created by our software development team called CloudCTF. Capture-the-flag (CTF) is a competition format that includes challenges across multiple cybersecurity topic areas, such as cryptography, networking, reconnaissance, web, reverse engineering, and others. Some introductory challenges introduce basic cybersecurity topics and may take only a few minutes for a student to solve. More difficult challenges might require a player to attack weakness in a modern cryptographic system, and may take several hours and advanced expertise. Our CloudCTF platform allows for tailored competitions composed of challenges created or imported by the person or group hosting the CTF.

11. Title: Virginia Cybersecurity Education Conference Teacher Sponsorships
PI: David Raymond
Lead Institution: Virginia Tech
Co-PIs & Institution: none
Funding Program: FY24 Workforce

Summary: The Virginia Cybersecurity Education Conference, gives 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 Central node to host the conference in Charlottesville, in collaboration with the University of Virginia.

12. Title: Research and Workforce Development in Quantum Tomography
PI: Wayne Scales 
Lead Institution: Virginia Tech
Co-PIs & Institution: none
Funding Program: FY24 Workforce 

Summary: In general, quantum tomography (QT) involves using measurements of an ensemble of quantum states to reconstruct a quantum state. It is a vital tool in Quantum Information Science and Engineering (QISE) and can be used to analyze or validate performance at the quantum gate level or an overall quantum communication channel. Characterization of a quantum communication channel is crucial for secure quantum communication systems. For example, it has been shown that the performance of Quantum Key Distribution (QKD) security protocols can be increased using QT.

13. Title: Approximate Floquet quantum error correcting codes
PI: Jamie Sikora
Lead Institution: Virginia Tech
Co-PIs & Institution: none
Funding Program: FY24 Workforce 

Summary: This project will fund PhD student, Ankith Mohan, to do an internship with Dr. Kishor Bharti, a Senior Scientist at A*STAR research institute in Singapore. During this project, Ankith will study an important problem within the study of quantum error correction (QEC). In particular, he will focus on creating a novel QEC code that uses fewer resources than current codes, is easier to experimentally implement, and is customized for specific noise models. Specifically, he will work towards the development of approximate Floquet quantum codes.

1. Title: CryptoQuest: An Interactive Animation Series for Teaching Cryptography, Post-Quantum Cryptography, and Cybersecurity using Augmented Reality
PI: Sherif Abdelhamid 
Lead Institution: Virginia Military Institute 
Co-PIs & Institution: Blain Patterson (VMI), Sarah Patterson (VMI), Gretchen Matthews (VT), Hiram Lopez (VT)
Funding Program: FY24 Innovation Program

Summary: The objective is to develop an interactive animation series to teach high school and university students' various cryptography, cybersecurity, and post-quantum cryptography concepts. The series will be designed to create an immersive learning experience that is engaging, fun, and rewarding. The project team will identify specific learning outcomes for each series episode and use student responses to tailor the content to their understanding levels. The project will align with the CCI mission to advance major research thrusts in cybersecurity and serve as a catalyst for Virginia's long-term leadership in cybersecurity. 

2. Title: One Step Closer to Ensuring Supply Chain Data Resiliency: Withstanding Cyber Disruption
PI: Myra Blanco
Lead Institution: Virginia Tech
Co-PIs & Institution: Zeb Bowden, Kevin Kefauver, Mike Mollenhauer (VT)
Funding Program: FY24 Innovation

Summary: This project will address gaps by enabling secure data exchange and sharing through a federated and decentralized data platform with a common governance structure. The platform will standardize the data format and provide governance to the data exchange process; as a result, what and how data is shared will be clearly defined. The platform will be available to all stakeholders in the supply chain, including those in the nautical and aerial sectors. The benefits to the supply chain resulting from the information connectivity provided by our platform are expected to include improved inventory control, shorter turnaround times on fulfillment, increased efficiencies of production development cycles, enhanced predictive insights into end users, and overall enhanced logistics capabilities to design, monitor, and adapt delivery plans.  Moreover, the data sharing platform will provide real-time visibility in the transport of goods, thereby improving the ability to forecast potential issues and strengthening supply chain resiliency. This platform will present opportunities to small businesses as well as large organizations at different levels of the supply chain. The equity and accessibility of the data exchanged through our platform will serve as an equalizing force to guarantee benefit to the public as well as the industries involved.

3. Title: In-Network Information Flow Control for Lightning-Fast Cross-Host Attack Prevention Enabled by Programmable Switches
PI: Peng Gao
Lead Institution: Virginia Tech
Co-PIs & Institution: Bo Ji (VT)
Funding Program: FY24 Innovation

Summary: In this project, we aim to develop a new network defense system that enables end-to-end network visibility across multiple hosts and enforces security decisions in real time to prevent sophisticated cross-host attacks. Our key idea is to develop a new tag-based network information flow control (IFC) mechanism to precisely track and control inter-host and intra-host information flows, through user-defined IFC tags. The critical challenge is to implement this IFC model and enforce it at the network level in an efficient way, without affecting the linespeed processing of high volumes of benign network traffic in modern enterprise networks. To achieve this, we will develop the first in-network realization of the IFC mechanism, by uniquely leveraging the emerging programmable switches [10] and eBPF [11]. The IFC enforcement occurs entirely within the network data plane, which is essential to lightning-fast attack prevention.