43 citations · 134 across the 52 of their papers we have counts for
9 papers · 1 filter
DP-TabICL: In-Context Learning with Differentially Private Tabular Data
Alycia N. Carey, Karuna Bhaila, Kennedy Edemacu +1
In-context learning (ICL) enables large language models (LLMs) to adapt to new tasks by conditioning on demonstrations of question-answer pairs and it has been shown to have compar…
Robust Fraud Detection via Supervised Contrastive Learning
Vinay M. S., Shuhan Yuan, Xintao Wu
Deep learning models have recently become popular for detecting malicious user activity sessions in computing platforms. In many real-world scenarios, only a few labeled malicious…
LogBERT: Log Anomaly Detection via BERT
Haixuan Guo, Shuhan Yuan, Xintao Wu
Detecting anomalous events in online computer systems is crucial to protect the systems from malicious attacks or malfunctions. System logs, which record detailed information of co…
Deep Learning for Insider Threat Detection: Review, Challenges and Opportunities
Shuhan Yuan, Xintao Wu
Insider threats, as one type of the most challenging threats in cyberspace, usually cause significant loss to organizations. While the problem of insider threat detection has been…
Insider Threat Detection via Hierarchical Neural Temporal Point Processes
Shuhan Yuan, Panpan Zheng, Xintao Wu +1
Insiders usually cause significant losses to organizations and are hard to detect. Currently, various approaches have been proposed to achieve insider threat detection based on ana…
Heterogeneous Gaussian Mechanism: Preserving Differential Privacy in Deep Learning with Provable Robustness
NhatHai Phan, Minh Vu, Yang Liu +4
In this paper, we propose a novel Heterogeneous Gaussian Mechanism (HGM) to preserve differential privacy in deep neural networks, with provable robustness against adversarial exam…