Rajeev Sahay

Orcid: 0000-0001-6823-1364

According to our database1, Rajeev Sahay authored at least 15 papers between 2019 and 2023.

Collaborative distances:
  • Dijkstra number2 of five.
  • Erdős number3 of four.

Timeline

Legend:

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PhD thesis 
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Links

On csauthors.net:

Bibliography

2023
Predicting Learning Interactions in Social Learning Networks: A Deep Learning Enabled Approach.
IEEE/ACM Trans. Netw., October, 2023

Defending Adversarial Attacks on Deep Learning-Based Power Allocation in Massive MIMO Using Denoising Autoencoders.
IEEE Trans. Cogn. Commun. Netw., August, 2023

How Potent are Evasion Attacks for Poisoning Federated Learning-Based Signal Classifiers?
Proceedings of the IEEE International Conference on Communications, 2023

2022
A Deep Ensemble-Based Wireless Receiver Architecture for Mitigating Adversarial Attacks in Automatic Modulation Classification.
IEEE Trans. Cogn. Commun. Netw., 2022

A Neural Network-Prepended GLRT Framework for Signal Detection Under Nonlinear Distortions.
IEEE Commun. Lett., 2022

Uncertainty Quantification-Based Unmanned Aircraft System Detection using Deep Ensembles.
Proceedings of the 95th IEEE Vehicular Technology Conference, 2022

2021
Mitigating Gradient-based Adversarial Attacks via Denoising and Compression.
CoRR, 2021

Robust Subject-Independent P300 Waveform Classification via Signal Pre-Processing and Deep Learning.
IEEE Access, 2021

Frequency-based Automated Modulation Classification in the Presence of Adversaries.
Proceedings of the ICC 2021, 2021

Robust Automatic Modulation Classification in the Presence of Adversarial Attacks.
Proceedings of the 55th Annual Conference on Information Sciences and Systems, 2021

Hyperspectral Image Target Detection Using Deep Ensembles for Robust Uncertainty Quantification.
Proceedings of the 55th Asilomar Conference on Signals, Systems, and Computers, 2021

2020
Non-Intrusive Detection of Adversarial Deep Learning Attacks via Observer Networks.
CoRR, 2020

Ensemble Noise Simulation to Handle Uncertainty about Gradient-based Adversarial Attacks.
CoRR, 2020

2019
A Computationally Efficient Method for Defending Adversarial Deep Learning Attacks.
CoRR, 2019

Combatting Adversarial Attacks through Denoising and Dimensionality Reduction: A Cascaded Autoencoder Approach.
Proceedings of the 53rd Annual Conference on Information Sciences and Systems, 2019


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