Combatting Adversarial Attacks through Denoising and Dimensionality Reduction: A Cascaded Autoencoder Approach. - Rajeev Sahay; Rehana Mahfuz; Aly El Gamal (CISS, 2019)

A Computationally Efficient Method for Defending Adversarial Deep Learning Attacks. - Rajeev Sahay; Rehana Mahfuz; Aly El Gamal (CoRR, 2019)

Ensemble Noise Simulation to Handle Uncertainty about Gradient-based Adversarial Attacks. - Rehana Mahfuz; Rajeev Sahay; Aly El Gamal (CoRR, 2020)

Non-Intrusive Detection of Adversarial Deep Learning Attacks via Observer Networks. - Kirthi Shankar Sivamani; Rajeev Sahay; Aly El Gamal (CoRR, 2020)

Hyperspectral Image Target Detection Using Deep Ensembles for Robust Uncertainty Quantification. - Rajeev Sahay; Daniel Ries; Joshua D. Zollweg; Christopher G. Brinton (ACSCC, 2021)

Robust Automatic Modulation Classification in the Presence of Adversarial Attacks. - Rajeev Sahay; David J. Love; Christopher G. Brinton (CISS, 2021)

Frequency-based Automated Modulation Classification in the Presence of Adversaries. - Rajeev Sahay; Christopher G. Brinton; David J. Love (ICC, 2021)

Robust Subject-Independent P300 Waveform Classification via Signal Pre-Processing and Deep Learning. - Rajeev Sahay; Christopher G. Brinton (IEEE Access, 2021)

Mitigating Gradient-based Adversarial Attacks via Denoising and Compression. - Rehana Mahfuz; Rajeev Sahay; Aly El Gamal (CoRR, 2021)

Uncertainty Quantification-Based Unmanned Aircraft System Detection using Deep Ensembles. - Rajeev Sahay; Gabriel C. Birch; Jaclynn J. Stubbs; Christopher G. Brinton (VTC Spring, 2022)

A Neural Network-Prepended GLRT Framework for Signal Detection Under Nonlinear Distortions. - Rajeev Sahay; Swaroop Appadwedula; David J. Love; Christopher G. Brinton (IEEE Commun. Lett., 2022)

A Deep Ensemble-Based Wireless Receiver Architecture for Mitigating Adversarial Attacks in Automatic Modulation Classification. - Rajeev Sahay; Christopher G. Brinton; David J. Love (IEEE Trans. Cogn. Commun. Netw., 2022)

How Potent are Evasion Attacks for Poisoning Federated Learning-Based Signal Classifiers? - Su Wang 007; Rajeev Sahay; Christopher G. Brinton (ICC, 2023)

Defending Adversarial Attacks on Deep Learning-Based Power Allocation in Massive MIMO Using Denoising Autoencoders. - Rajeev Sahay; Minjun Zhang; David J. Love; Christopher G. Brinton (IEEE Trans. Cogn. Commun. Netw., 2023)

Predicting Learning Interactions in Social Learning Networks: A Deep Learning Enabled Approach. - Rajeev Sahay; Serena Nicoll; Minjun Zhang; Tsung-Yen Yang; Carlee Joe-Wong; Kerrie A. Douglas; Christopher G. Brinton (IEEE/ACM Trans. Netw., 2023)

Revolutionizing AI-Assisted Education with Federated Learning: A Pathway to Distributed, Privacy-Preserving, and Debiased Learning Ecosystems. - Anurata Prabha Hridi; Rajeev Sahay; Seyyedali Hosseinalipour; Bita Akram (AAAI Spring Symposia, 2024)

Privacy-Preserving Distributed Link Predictions Among Peers in Online Classrooms Using Federated Learning. - Anurata Prabha Hridi; Muntasir Hoq; Zhikai Gao; Collin Lynch; Rajeev Sahay; Seyyedali Hosseinalipour; Bita Akram (EDM, 2025)

Mitigating Evasion Attacks in Federated Learning Based Signal Classifiers. - Su Wang 007; Rajeev Sahay; Adam Piaseczny; Christopher G. Brinton (IEEE Trans. Netw. Sci. Eng., 2025)

The Transition from Centralized Machine Learning to Federated Learning for Mental Health in Education: A Survey of Current Methods and Future Directions. - Maryam Ebrahimi; Rajeev Sahay; Seyyedali Hosseinalipour; Bita Akram (CoRR, 2025)