14 citations · 22 across the 7 of their papers we have counts for
7 papers
Robust and Explainable Divide-and-Conquer Learning for Intrusion Detection
Yan Zhou, Kevin Hamlen, Michael De Lucia +5
Machine learning-based intrusion detection requires complex models to capture patterns in high-dimensional, noisy, and class-imbalanced raw network traffic, yet deploying such mode…
SMOTE-DP: Improving Privacy-Utility Tradeoff with Synthetic Data
Yan Zhou, Bradley Malin, Murat Kantarcioglu
Privacy-preserving data publication, including synthetic data sharing, often experiences trade-offs between privacy and utility. Synthetic data is generally more effective than dat…
Using AI Uncertainty Quantification to Improve Human Decision-Making
Laura R. Marusich, Jonathan Z. Bakdash, Yan Zhou +1
AI Uncertainty Quantification (UQ) has the potential to improve human decision-making beyond AI predictions alone by providing additional probabilistic information to users. The ma…
Multi-concept adversarial attacks
Vibha Belavadi, Yan Zhou, Murat Kantarcioglu +1
As machine learning (ML) techniques are being increasingly used in many applications, their vulnerability to adversarial attacks becomes well-known. Test time attacks, usually laun…
Improving Fairness of AI Systems with Lossless De-biasing
Yan Zhou, Murat Kantarcioglu, Chris Clifton
In today's society, AI systems are increasingly used to make critical decisions such as credit scoring and patient triage. However, great convenience brought by AI systems comes wi…
Does Explainable Artificial Intelligence Improve Human Decision-Making?
Yasmeen Alufaisan, Laura R. Marusich, Jonathan Z. Bakdash +2
Explainable AI provides insight into the "why" for model predictions, offering potential for users to better understand and trust a model, and to recognize and correct AI predictio…
Breaking Transferability of Adversarial Samples with Randomness
Yan Zhou, Murat Kantarcioglu, Bowei Xi
We investigate the role of transferability of adversarial attacks in the observed vulnerabilities of Deep Neural Networks (DNNs). We demonstrate that introducing randomness to the…