| {URET}: Universal Robustness Evaluation Toolkit (for Evasion) K Eykholt, T Lee, D Schales, J Jang, I Molloy 32nd USENIX Security Symposium (USENIX Security 23), 3817-3833, 2023 | 4 | 2023 |
| A Study of the Effects of Transfer Learning on Adversarial Robustness P Vaishnavi, K Eykholt, A Rahmati Transactions on Machine Learning Research, 0 | | |
| Accelerating certified robustness training via knowledge transfer P Vaishnavi, K Eykholt, A Rahmati Advances in Neural Information Processing Systems 35, 5269-5281, 2022 | 4 | 2022 |
| Adaptive robustness certification against adversarial examples K Eykholt, T Lee, J Jang, S Wang, IM Molloy US Patent App. 17/113,927, 2022 | 1 | 2022 |
| Adaptive verifiable training using pairwise class similarity S Wang, K Eykholt, T Lee, J Jang, I Molloy Proceedings of the AAAI Conference on Artificial Intelligence 35 (11), 10201 …, 2021 | 3 | 2021 |
| Ares: A system-oriented wargame framework for adversarial ml F Ahmed, P Vaishnavi, K Eykholt, A Rahmati 2022 IEEE Security and Privacy Workshops (SPW), 73-79, 2022 | 5 | 2022 |
| Benchmarking the Effect of Poisoning Defenses on the Security and Bias of Deep Learning Models N Baracaldo, F Ahmed, K Eykholt, Y Zhou, S Priya, T Lee, S Kadhe, M Tan, ... 2023 IEEE Security and Privacy Workshops (SPW), 45-56, 2023 | | 2023 |
| Benchmarking the Effect of Poisoning Defenses on the Security and Bias of Deep Learning Models NB Angel, F Ahmed, K Eykholt, Y Zhou, S Priya, T Lee, SR Kadhe, M Tan, ... IEEE Symposium on Security and Privacy, 2023 | | 2023 |
| Benchmarking the Effect of Poisoning Defenses on the Security and Bias of the Final Model N Baracaldo, K Eykholt, F Ahmed, Y Zhou, S Priya, T Lee, S Kadhe, Y Tan, ... Workshop on Trustworthy and Socially Responsible Machine Learning, NeurIPS 2022, 2022 | 1 | 2022 |
| Benchmarking the Effect of Poisoning Defenses on the Security and Bias of the Final Model NB Angel, K Eykholt, F Ahmed, Y Zhou, S Priya, T Lee, SR Kadhe, M Tan, ... Annual Conference on Neural Information Processing Systems, 2022 | 1 | 2022 |
| Can attention masks improve adversarial robustness? P Vaishnavi, T Cong, K Eykholt, A Prakash, A Rahmati International Workshop on Engineering Dependable and Secure Machine Learning …, 2020 | 10 | 2020 |
| Constraining neural networks for robustness through alternative encoding K Eykholt, T Lee, IM Molloy, J Jang US Patent 11,847,555, 2023 | 2 | 2023 |
| Designing adversarially resilient classifiers using resilient feature engineering K Eykholt, A Prakash arXiv preprint arXiv:1812.06626, 2018 | 3 | 2018 |
| Designing and Evaluating Physical Adversarial Attacks and Defenses for Machine Learning Algorithms K Eykholt | 2 | 2019 |
| DeTA: Minimizing Data Leaks in Federated Learning via Decentralized and Trustworthy Aggregation PC Cheng, K Eykholt, Z Gu, H Jamjoom, KR Jayaram, E Valdez, A Verma Proceedings of the Nineteenth European Conference on Computer Systems, 219-235, 2024 | 1 | 2024 |
| EdgeTorrent: Real-time Temporal Graph Representations for Intrusion Detection IJ King, X Shu, J Jang, K Eykholt, T Lee, HH Huang Proceedings of the 26th International Symposium on Research in Attacks …, 2023 | 2 | 2023 |
| Ensuring Authorized Updates in Multi-user {Database-Backed} Applications K Eykholt, A Prakash, B Mozafari 26th USENIX Security Symposium (USENIX Security 17), 1445-1462, 2017 | 5 | 2017 |
| Federated learning with partitioned and dynamically-shuffled model updates Z Gu, JK Radhakrishnan, A Verma, E Valdez, PC Cheng, HT Jamjoom, ... US Patent App. 17/323,099, 2022 | | 2022 |
| Graph exploration framework for adversarial example generation T Lee, K Eykholt, DL Schales, J Jang, IM Molloy US Patent App. 17/536,059, 2023 | 1 | 2023 |
| Graph neural network (gnn) training using meta-path neighbor sampling and contrastive learning D She, X Shu, K Eykholt, J Jang US Patent App. 17/480,012, 2023 | 1 | 2023 |
