Research Interests
My research interests include AI and network security, data privacy, and blockchain systems, with the focus on AI security and applications, DNN model security, data privacy, blockchain and network security, and blockchain system development.
|
Recent Related Work:
![]() |
We introduce a new generative network with separated latent features to construct attacks and defenses for a “smile” detector. |
![]() |
We propose a new adversarial attack method called GeoFool, GeoFool can generate Common knowledge and individual knowledge. |
![]() |
We propose a new generative adversarial attack algorithm with graph-convolutional condition GAN, which aims towards realistic and strong adversarial attack against 3D point cloud deep learning model. |
![]() |
We introduce a DAE-GAN model and a data-augmentation-based training algorithm for detecting abnormal traffic. |
Recent Related Publications:
- ACM Multimedia 2021: Towards Multiple Black-boxes Attack via Adversarial Example Generation Network
- ACM Computing Surveys: A Survey of IoT Applications in Blockchain Systems: Architecture, Consensus and Traffic Modeling
- ACM CCS 2019: Power Adjusting and Bribery Racing: Novel Mining Attacks in the Bitcoin System
- IEEE J-SAC 2019: Power Control Identification: A Novel Sybil Attack Detection Scheme in VANETs Using RSSI
- ACM UBICOMP 2018: Location Privacy-Preserving Data Recovery for Mobile Crowdsensing
- IEEE Network 2018: Vehicle Safety Improvement Through Deep Learning and Mobile Sensing
- IEEE INFOCOM 2017: FloodDefender: Protecting Data and Control Plane Resources under SDN-aimed DoS Attacks
: