Our research program is dedicated to developing efficient and secure network and system architectures that facilitate the rapid, reliable, private, and secure delivery and processing of information. The proliferation of heterogeneous mobile and wireless devices, such as smartphones and IoT devices, generates an ever-increasing volume of data, while available spectrum and bandwidth resources become increasingly scarce. Simultaneously, advancements in hardware and software capabilities, coupled with improved energy efficiency, empower these devices to not only communicate and network but also sense, compute, and interact with the physical world.
Networks are evolving into multifunctional, intelligent infrastructures supporting integrated communication, sensing, data storage, and computation. This transformation enables a wide range of exciting applications and services, including Augmented/Virtual Reality (AR/VR or XR), Connected and Autonomous Vehicles (CAVs), and Collaborative Multi-Agent Systems (e.g., robotic fleets or embodied AI agents). To support these applications, which often require edge computing for low-latency processing, new network architectures, protocols, and algorithms are essential. Moreover, the rapid advancements in Artificial Intelligence (AI) and Machine Learning (ML) present opportunities to enhance system intelligence not only at the application level but also within the network fabric itself.
However, these novel computing paradigms also introduce new vulnerabilities that can be exploited by malicious actors for economic gain or societal harm. The widespread collection and storage of personal information across interconnected systems make individuals more susceptible to cyberattacks and data breaches. The rapid pace of device deployment often prioritizes market competitiveness over security, leading to inherent vulnerabilities in their designs. As a result, the very technologies we develop can have unintended negative consequences, including economic loss and threats to public safety. To prevent a cycle of continuous vulnerability and remediation, it is imperative to prioritize security, privacy, and safety in the design and development of networked intelligent systems.
Research Methodology
Our goal is to develop both solid foundations and practical mechanisms for performance, and security/privacy/safety assurance in emerging networked intelligent systems, to make them dependable and trustworthy. Our research is devoted not only to make them resilient to malicious attacks, but also to promote proactive built-in security protection in their early design. Our research philosophy is to bring together theory and practice. On the theoretical side, we may leverage tools from communications and networking, signal processing, optimization, machine learning, algorithm design, and applied cryptography. On the practical side, we may investigate a variety of applications and make use of real-world networked devices such as vehicles, drones, IoT platforms, and various datasets, experimental/simulation platforms such as software-defined radios, etc. We always keep an open mind to new problems and toolsets, and are prepared to challenge existing and well-established assumptions.
Research Topics/Projects
Ongoing Research Topics
Security of NextG Radio Access Networks
Next-generation (NextG) cellular systems will be designed with awareness, intelligence, and flexibility to support diverse use cases such as telepresence, immersive sports, connected intelligent machines and interacting robots, precision healthcare, and others. These attributes will be realized through groundbreaking technologies in uncharted frequency bands, e.g., millimeter-wave (mmWave) and Teraherz (THz) bands, virtualized core and radio access network (RAN) architectures, new spectrum sharing models, and powerful machine learning algorithms that optimally manage resources. Various AI/ML approaches can be adopted for NextG RAN management and control, such as reinforcement learning (RL). However, RAN resource sharing exposes the network to new security threats that target the robustness of the decision-making process. Our research aims to study RAN resource management algorithms in an adversarial setting, mitigate user privacy leakage, and develop mechanisms to ensure that RAN policies are designed to meet service level agreements.
Security of Integrated Sensing and Communications (ISAC)
To meet the diverse service demands of NextG applications, NextG networks will support new wireless capabilities in the mmWave and THz bands that go beyond communications and simultaneously support high-resolution sensing. By integrating sensing into the communications network, the network acts as a "radar" sensor, using its own radio signals to sense and comprehend the physical world in which it operates. The sensing data can then be leveraged to enhance the network’s own operations, augment existing services such as XR and digital twinning, and enable new services such as gesture/activity recognition, imaging and environment reconstruction. While ISAC offers significant performance benefits, its security and resiliency issues have been largely under-explored. Our research investigates the security of ISAC including novel vulnerabilities and defense mechanisms, and exploit ISAC to build secure-by-design NextG applications.
Secure Perception for Autonomous Systems
Autonomous Systems (AS), such as self-driving cars, robotic agents, and surveillance systems, rely heavily on sensors to perceive their surroundings and make informed, autonomous decisions. The security of these systems has become increasingly critical, as malicious actors can exploit vulnerabilities in the perception pipeline, leading to potentially catastrophic consequences. Our research focuses on studying the security vulnerabilities of sensor perception modules in autonomous systems, including their sensing mechanisms along with the ML-based object detection and tracking algorithms. For example, an adversary can remotely inject deceptive patterns into camera feeds, creating or altering objects in the perceived environment, causing unsafe control actions. To counter such threats, we will introduce a novel defense framework that leverages spatiotemporal consistency checks, which is agnostic to the specific sensing modality or attack vector.
Funding support: Army Research Office (ARO), "Radio-Frequency Interference for Fault Injection and Sensor Manipulation", Army Research Office, 08/2021 - 01/2025, Co-PI (Lead: Virginia Tech).Edge-Assisted Cooperative Perception
Connected and Autonomous Vehicles (CAVs) will revolutionize transportation, promising enhanced safety and efficiency. Vehicle-to-Everything (V2X) connectivity enables vehicles and infrastructure to share information, fostering cooperative perception (CP) and enabling groundbreaking applications like cooperative driving, dynamic map updates, platooning, and infrastructure-assisted traffic management. Safety-critical CAV applications demand stringent performance requirements, including high perception accuracy, low end-to-end latency, and high reliability. Edge-assisted CP systems face challenges in scalable raw sensor data sharing from multiple vehicles and adapting to dynamic network conditions. Our research addresses these challenges by proposing a goal-oriented (semantic) communications framework, which leverages ML techniques to intelligently process and extract the most relevant information from the sensor data, and jointly optimizes the computation and communication resources at the vehicles and edge to meet the end-goals of CP. This research has broader implications, extending beyond CAVs to various multi-agent systems.
Past Research Projects
1. Enhancing Wireless Network Coexistence via Interference Cancellation
In the increasingly crowded spectrum sharing mechanisms are desired to enable coexistence among disparate multi-hop wireless networks. Cross-technology interference (CTI) is widespread, detrimental to network performance (e.g., between LTE and WiFi networks). Current approaches focus on interference avoidance, separating transmissions in frequency, time, or space, rather than reducing or eliminating interference. Advancements in physical layer technologies like Multiple Input Multiple Output (MIMO) interference cancellation (IC) enable interference-free concurrent transmissions. This project seeks to develop new models and methodologies to theoretically quantify the performance limit of cross-technology IC, as well as designing practical protocols to enable interference-free coexistence.
Funding support: National Science Foundation, CAREER: Toward Cooperative Interference Mitigation for Heterogeneous Multi-hop MIMO Wireless Networks, 7/1/2014-6/31/2019, PI (Project website).2. Enhancing Wireless Network Performance with Reconfigurable Antennas
As the number of wireless devices and their data demands surge, multi-hop wireless networks (MWNs) require higher capacity, reliability, and quality-of-service (QoS). Traditional approaches, focusing on network-layer mechanisms, struggle to address the fundamental challenge of unreliable wireless channels. Our research systematically explores antenna-level reconfigurability to optimize end-to-end performance and QoS in MWNs. We lay the theoretical foundations and develop practical protocols. Additionally, we leverage online machine learning algorithms to adapt to dynamic link conditions and optimize end-to-end scheduling and routing. This research has impact on networks operating in both sub-6GHz and mmWave bands.
Funding support: Office of Naval Research (ONR), "Toward High Performance Tactical Multi Hop Wireless Networks via Exploiting Antenna Reconfigurability", 6/1/2016-5/31/2019, PI.3. Wireless Security: Automatic Trust Establishment for IoT
Wireless networks are critical for collecting data from IoT sensors, such as wearable health monitors and security cameras. Ensuring the security, integrity, and availability of these communications is paramount for user safety and privacy. Establishing initial trust, involving mutual authentication and key agreement, is a fundamental step. Traditional methods, like manual key input or pre-loaded secrets, face scalability, usability, and security challenges. PKI-based solutions struggle with key revocation, especially in intermittently connected environments. Our research develops in-band solutions to automate initial trust establishment and trust evolution. This approach requires only a common radio interface, eliminating the need for out-of-band channels or additional hardware. By addressing these challenges, we aim to enhance the security and reliability of wireless IoT networks.
Funding support:4. Security and Privacy of Dynamic Spectrum Access
The growing demand for wireless services has led to spectrum scarcity and the need for coexistence of diverse wireless technologies. This coexistence poses significant challenges due to heterogeneity, scale, and lack of coordination. Traditional approaches to spectrum access enforcement, relying on trusted hardware or dedicated devices, are costly and incompatible with legacy devices. To address these issues, we propose a novel framework for secure, efficient, and privacy-preserving spectrum access coordination. We also explore a crowdsourcing-based approach for spectrum etiquette enforcement, leveraging the collective power of cognitive radio devices to detect and deter spectrum misuse without relying on centralized infrastructure.
Funding support:
5. Secure and Privacy-Preserving Data Sharing and Analytics
Cloud computing offers scalable and cost-effective data storage and processing, but raises significant security and privacy concerns. Our research aims to address these challenges by developing privacy-preserving solutions for sensitive data stored in public clouds. We focus on: 1) Data Possession Proofs: Ensuring data owners retain control over their data in the cloud. 2) Privacy-Preserving Search: Enabling secure and private data search within cloud environments. 3) Collaborative Cloud Computing: Facilitating secure and private collaborative computations on cloud data. Additionally, we explore novel data purturbation techniques to balance privacy and utility in scenarios where data is collected for analytics and personalized services. We investigate context-aware privacy notions and mechanisms to enhance the utility-privacy trade-off, leveraging statistical and information-theoretic approaches.
Funding support:6. Secure and Resilient Vehicle Platooning
Vehicular platooning promises to revolutionize transportation. While it offers opportunities for increased efficiency and safety, it also introduces new security risks. Our research aims to secure automated vehicles by: 1) Identifying vulnerabilities: We examine vulnerabilities in inter- and intra-vehicle communication and control systems. 2) Developing defenses: We propose practical, low-overhead defense mechanisms, including physical layer security techniques. 3) Supporting phased deployment: We consider a three-phase deployment: local autonomy, vehicle-to-vehicle communication, and vehicle-to-infrastructure communication. By addressing these challenges, we aim to ensure a safe and secure future for automated transportation.
Funding support: National Science Foundation, TWC: Medium: Secure and Resilient Vehicular Platooning, 8/1/2014-7/31/2019, Co-PIProject Website: Security in autonomous vehicular transportation
News coverage: The Epoch Times,Driverless transportation, The Atlantic, 163 Tech News (网易科技新闻), 爬车网
7. Security of Unmanned Aerial Systems (UAS)
Unmanned Aerial Systems (UAS) offer significant potential but also pose serious security and privacy risks. As UAS proliferation continues, regulatory agencies struggle to integrate them safely into airspace and enforce regulations. This project aims to develop automated systems to detect and neutralize UAS that violate controlled airspace. For example, we propose techniques to detect and localize passive drones that do not actively emit signals, by exploiting existing cellular network infrastructure. We will develop both offensive and defensive strategies, leveraging expertise in UAS flight control, CPS security, wireless communications, reinforcement learning and intelligent control. Our goal is to safeguard airspace and ensure the safe operation of UAS.
Funding support: National Science Foundation, SaTC: CORE: Medium: Collaborative: Enforcement of Geofencing Policies for Commercial Unmanned Aircraft Systems, 09/01/2018 - 08/31/2021, PI.8. Cyber-Resilience for Power Systems
This project aims to secure the integration of Distributed Energy Resources (DER) and power aggregators in the electric grid. We propose a Blockchain-based overlay network combined with a Model-Assisted Machine Learning (MAML) framework. This approach enables: 1) Enhanced Security: Mitigates risks in current network and C2 protocols. 2) Proactive Defense: Detects and responds to attacks targeting DER and distribution systems, in a privacy-preserving manner. 3) Grid Resilience: Automates DER enrollment in grid services like demand response and frequency control. 4) Legacy Device Protection: Develops a plug-and-play security module for legacy DER. By combining blockchain and AI, we aim to create a more secure and resilient electric grid.
Funding support: U.S. Department of Energy (DOE), Achieving Cyber-Resilience for Power Systems using a Learning Model-Assisted Blockchain Framework, 06/01/2021 - 6/30/2023, Co-PI (Lead: Virginia Tech).
We thank the generous support of:
