Demo Videos
A Demo of Data Scheduling for Ultra-Reliable Low Latency Communications in O-RAN
Hermes: Boosting the Performance of ML-Based IDS through Geometric Feature Learning
VEHIGAN: Generative Adversarial Networks forAdversarially Robust V2X Misbehavior DetectionSystems
StarCast: A Secure and Efficient Multicast Scheme in LEO Satellite Networks
A Demo of Interference Mitigation for Automotive Radar
Toward Enforceable Data Usage Control in Cloud-based IoT Systems
We developed PrivacyGuard, a data privacy platform that empowers data owners with full control over the access and actual usage of their private data by any data consumer.
Main Publication: Y. Xiao, N. Zhang, J. Li, W. Lou, Y. T. Hou, "PrivacyGuard: Enforcing Private Data Usage Control with Blockchain and Off-chain Contract Execution," ESORICS 2020, September 2020
Attack and Defense to Machine-Learning-Based System in VR Battlefield
Main publications:
N. Wang, Y. Chen, Y. Hu, W. Lou, Y.T. Hou, "MANDA: On Adversarial Example Detection for Network Intrusion Detection System," IEEE INFOCOM 2021, May 2021, Vancouver, BC, Canada.
N. Wang, Y. Xiao, Y. Chen, Y. Hu, W. Lou, Y.T. Hou, "FLARE: Defending Federated Learning against Model Poisoning Attacks via Latent Space Representations," ACM ASIACCS 2022, May 2022, Nagasaki, Japan.