Research Projects
Security, Privacy, and Trust in Immersive Systems
This project investigates emerging security, privacy, and trust challenges in immersive systems. The research aims to identify attack surfaces, understand risks to users and their environments, and develop practical defenses for secure and trustworthy augmented, virtual, and extended-reality technologies.
Selected publications
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2026
N. Hoque, “What AR Glasses Leak: Physical Scene Inference from Encrypted Traffic,” in Proceedings of the Workshop on Enhancing Security, Privacy, and Trust in Extended Reality (XR) Systems, co-located with the ACM SIGMOBILE Annual International Conference on Mobile Computing and Networking (MobiCom), Austin, TX, Oct. 2026.
Trustworthy RF Intelligence
This project develops reliable artificial-intelligence methods for understanding radio-frequency signals in dynamic and previously unseen environments. The research explores language-guided and zero-shot wireless signal recognition so that next-generation wireless receivers can identify new signal types without requiring labeled training examples for every signal type.
Selected publications
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2026
I. C. Llorente and N. Hoque, “Language-Based Zero-Shot Modulation Recognition for Next-Generation IoT Receivers,” in Proceedings of the IEEE International Conference on Artificial Intelligence, Blockchain, and Internet of Things (AIBThings), Mount Pleasant, MI, Sep. 2026.
Cyber Deception and Adaptive Defense in Wireless Systems
This project investigates proactive security mechanisms that dynamically adapt wireless-system behavior to create uncertainty for adversaries. The research develops and evaluates moving-target defenses, signal-obfuscation methods, and adaptive strategies for countering attacks against wireless signal-classification systems.
Selected publications
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2024
N. Hoque and H. Rahbari, “Deep Learning Models as Moving Targets to Counter Modulation Classification Attacks,” in Proceedings of the IEEE International Conference on Computer Communications (INFOCOM), Vancouver, Canada, May 2024.
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2023
N. Hoque and H. Rahbari, “Circumventing the Defense against Modulation Classification Attacks,” in Proceedings of the ACM Conference on Security and Privacy in Wireless and Mobile Networks (WiSec), pp. 377–382, Guildford, United Kingdom, May–June 2023.
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2021
N. Hoque and H. Rahbari, “POSTER: A Tough Nut to Crack: Attempting to Break Modulation Obfuscation,” in Proceedings of the ACM SIGSAC Conference on Computer and Communications Security (CCS), pp. 2402–2404, Nov. 2021.
Wireless Protocol Security
This project systematically analyzes wireless protocols to identify security weaknesses that arise before authentication and during connection establishment. The research examines relay, spoofing, denial-of-service, and man-in-the-middle attacks in Wi-Fi systems and develops practical defenses for securing concurrent connection attempts.
Selected publications
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2025
N. Hoque and H. Rahbari, “Locking Down Relay and Spoofing Attacks during Concurrent Connection Establishments in 802.11ax,” IEEE Transactions on Dependable and Secure Computing, vol. 22, no. 6, pp. 7287–7301, Dec. 2025.
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2023
N. Hoque and H. Rahbari, “Countering Relay and Spoofing Attacks in the Connection Establishment Phase of Wi-Fi Systems,” in Proceedings of the ACM Conference on Security and Privacy in Wireless and Mobile Networks (WiSec), pp. 275–285, Guildford, United Kingdom, May–June 2023.
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2022
N. Hoque, H. Rahbari, and C. Rezendes, “Systematically Analyzing Vulnerabilities in the Connection Establishment Phase of Wi-Fi Systems,” in Proceedings of the IEEE Conference on Communications and Network Security (CNS), pp. 64–72, Austin, TX, Oct. 2022.
Datasets and Testbeds
Wi-Fi Pre-Authentication Attack and Defense Testbed
This testbed supports reproducible experiments involving relay and spoofing attacks during the connection- establishment phase of Wi-Fi systems. It provides an environment for implementing attacks, evaluating pre-authentication defenses, and measuring their effectiveness under realistic wireless conditions.
Obfuscated Wireless-Signal Dataset and Evaluation Testbed
This resource contains obfuscated wireless signals and an experimental testbed for evaluating modulation- classification attacks, modulation-obfuscation methods, and defensive countermeasures. It supports research on adversarial robustness and adaptive defense for AI-enabled wireless systems.