Deep diffusion-based forecasting of COVID-19 by incorporating network-level mobility information. - Padmaksha Roy; Shailik Sarkar; Subhodip Biswas; Fanglan Chen; Zhiqian Chen; Naren Ramakrishnan; Chang-Tien Lu (ASONAM, 2021)

Deep Correlation-Aware Kernelized Autoencoders for Anomaly Detection in Cybersecurity. - Padmaksha Roy (CoRR, 2023)

A Survey of Data Security: Practices from Cybersecurity and Challenges of Machine Learning. - Padmaksha Roy; Jaganmohan Chandrasekaran; Erin Lanus; Laura J. Freeman; Jeremy Werner (CoRR, 2023)

Improving Intrusion Detection with Domain-Invariant Representation Learning in Latent Space. - Padmaksha Roy; Tyler Cody; Himanshu Singhal; Kevin Choi; Ming Jin 002 (CoRR, 2023)

Latent Space Correlation-Aware Autoencoder for Anomaly Detection in Skewed Data. - Padmaksha Roy; Himanshu Singhal; Timothy J. O'Shea; Ming Jin 002 (PAKDD (1), 2024)

Defense against Joint Poison and Evasion Attacks: A Case Study of DERMS. - Zain ul Abdeen; Padmaksha Roy; Ahmad Al-Tawaha; Rouxi Jia; Laura J. Freeman; Peter A. Beling; Chen-Ching Liu; Alberto L. Sangiovanni-Vincentelli; Ming Jin 002 (CoRR, 2024)

Model Residuals as Shields: A Two-Level Formulation to Defend Smart Grids From Poisoning Attacks. - Tung-Wei Lin; Padmaksha Roy; Yi Zeng 005; Ming Jin 002; Ruoxi Jia 001; Chen-Ching Liu; Alberto L. Sangiovanni-Vincentelli (IEEE Internet Things J., 2025)