Publications (24)
DEFENDCLI: {Command-Line} Driven Attack Provenance Examination
Peilun Wu, Nan Sun, Nour Moustafa +2
Endpoint Detection and Response (EDR) solutions embrace the method of attack provenance graph to discover unknown threats through system event correlation. However, this method sti…
Less is More: Geometric Unlearning for LLMs with Minimal Data Disclosure
Chenchen Tan, Xinghao Li, Shujie Cui +3
As large language models (LLMs) are increasingly deployed in real-world systems, they must support post-hoc removal of specific content to meet privacy and governance requirements.…
FedMLAC: Mutual Learning Driven Heterogeneous Federated Audio Classification
Jun Bai, Rajib Rana, Di Wu +5
Federated Learning (FL) offers a privacy-preserving framework for training audio classification (AC) models across decentralized clients without sharing raw data. However, Federate…
Meta-Guardian: An Early Evaluation of an On-device Application to Mitigate Psychography Data Leakage in Immersive Technologies
Keshav Sood, Sanjay Selvaraj, Youyang Qu
The use of Immersive Technologies has shown its potential to revolutionize many sectors such as health, entertainment, education, and industrial sectors. Immersive technologies suc…
An Efficient and Reliable Asynchronous Federated Learning Scheme for Smart Public Transportation
Chenhao Xu, Youyang Qu, Tom H. Luan +3
Since the traffic conditions change over time, machine learning models that predict traffic flows must be updated continuously and efficiently in smart public transportation. Feder…
Federated Balanced Learning
Jiaze Li, Haoran Xu, Wanyi Wu +9
Federated learning is a paradigm of joint learning in which clients collaborate by sharing model parameters instead of data. However, in the non-iid setting, the global model exper…