Cybersecurity, cryptography, and privacy research aims at characterizing and detecting vulnerabilities and creating innovative defenses to protect computing systems, networks, and data that our modern life critically depends on.
The research interests of our large group of outstanding security faculty cover a wide collection of critical and challenging problems, including software and system security, network security, machine learning security, quantum cryptography, blockchain, hardware security, data protection and privacy, and national security.
Some of the research themes in this area include:
- Deployable software and system security aims at closing the gap between theory and practice by designing deployment-grade code scanners and system monitors.
- Internet security protocols theme improves protocols by leveraging machine learning techniques to minimize error-prone manual efforts.
- Wireless network and CPS security focuses on identifying vulnerabilities and developing security and privacy mechanisms to secure next-generation wireless networks and cyber-physical systems, including resource-constrained wireless sensors, IoT environments, and smart spaces.
- Quantum cryptography studies the benefits and limitations of using quantum protocols for cryptography. While quantum computers threaten the security of modern protocols, quantum cryptography can offer alternatives with remarkable security guarantees.
- Security in deep learning identifies, detects, and prevents various types of adversarial attacks in deep learning models.
- Proactive defense development is to build proactive security solutions, e.g., moving target defense, anomaly detection, and defensive deception, to resist new exploits.
Here are some ongoing security research projects:
SecurityKG: This project automates cyber threat intelligence gathering and management using AI techniques.
DNSSECFixer: This project leverages machine learning techniques to automatically fix incorrectly configured and insecure domains.
Insider Threat Detection: This Commonwealth Cyber Initiative (CCI) funded project invents probabilistic approaches for identifying high-risk insider behaviors.
CryptoGuard: This NSF and ONR funded project designs deployment-quality secure coding solutions.
S2Guard: A CNSR Lab project funded by NSF and DHS on the security and safety in autonomous vehicles via multi-layer protection and isolation of safety-critical components, anomaly detection, and runtime safety enforcement.
Securing IoT Systems: Multiple CNSR Lab projects on IoT security and privacy, complementing IoT frameworks to maintain integrity and privacy.
Blockchain-based decentralized wireless spectrum management: A CNSR Lab effort develops a zero-trust network architecture leveraging blockchains and smart contracts for open and fair wireless spectrum access.
Energy Centric Wireless Sensor Nodes for Smart Farms: This multidisciplinary effort with CS, ECE, and APS (Animal and Poultry Science) addresses resource management, data security, and validation on a real testbed and leverages VT’s SmartFarm.
Detection of Backdoor Attacks in Deep Neural Networks: This tClab project considers backdoor attacks and other poisonous attacks that disrupt normal DL operations, including convolutional neural networks, deep reinforcement learning, and transfer learning.
Foureye: This research develops defensive deception techniques using hypergame theory for tactical networks.
- Resilient Cyber-Physical Systems: This tClab research develops defense mechanisms to provide proactive defense capabilities using deep reinforcement learning.
- Multidimensional DL in Adversarial Environments: This tClab project quantifies multidimensional uncertainty for defending against adversarial examples in deep learning.