Too Big to FAIL: What You Need to Know Before Attacking a Machine Learning System. - Tudor Dumitras; Yigitcan Kaya; Radu Marginean; Octavian Suciu (Security Protocols Workshop, 2018)

When Does Machine Learning FAIL? Generalized Transferability for Evasion and Poisoning Attacks. - Octavian Suciu; Radu Marginean; Yigitcan Kaya; Hal Daumé III; Tudor Dumitras (USENIX Security Symposium, 2018)

Security Analysis of Deep Neural Networks Operating in the Presence of Cache Side-Channel Attacks. - Sanghyun Hong 001; Michael Davinroy; Yigitcan Kaya; Stuart Nevans Locke; Ian Rackow; Kevin Kulda; Dana Dachman-Soled; Tudor Dumitras (CoRR, 2018)

How to Stop Off-the-Shelf Deep Neural Networks from Overthinking. - Yigitcan Kaya; Tudor Dumitras (CoRR, 2018)

Shallow-Deep Networks: Understanding and Mitigating Network Overthinking. - Yigitcan Kaya; Sanghyun Hong 001; Tudor Dumitras (ICML, 2019)

Terminal Brain Damage: Exposing the Graceless Degradation in Deep Neural Networks Under Hardware Fault Attacks. - Sanghyun Hong 001; Pietro Frigo; Yigitcan Kaya; Cristiano Giuffrida; Tudor Dumitras (USENIX Security Symposium, 2019)

How to 0wn the NAS in Your Spare Time. - Sanghyun Hong 001; Michael Davinroy; Yigitcan Kaya; Dana Dachman-Soled; Tudor Dumitras (ICLR, 2020)

On the Effectiveness of Mitigating Data Poisoning Attacks with Gradient Shaping. - Sanghyun Hong 001; Varun Chandrasekaran; Yigitcan Kaya; Tudor Dumitras; Nicolas Papernot (CoRR, 2020)

On the Effectiveness of Regularization Against Membership Inference Attacks. - Yigitcan Kaya; Sanghyun Hong 001; Tudor Dumitras (CoRR, 2020)

A Panda? No, It's a Sloth: Slowdown Attacks on Adaptive Multi-Exit Neural Network Inference. - Sanghyun Hong 001; Yigitcan Kaya; Ionut-Vlad Modoranu; Tudor Dumitras (ICLR, 2021)

When Does Data Augmentation Help With Membership Inference Attacks? - Yigitcan Kaya; Tudor Dumitras (ICML, 2021)

Qu-ANTI-zation: Exploiting Quantization Artifacts for Achieving Adversarial Outcomes. - Sanghyun Hong 001; Michael-Andrei Panaitescu-Liess; Yigitcan Kaya; Tudor Dumitras (NeurIPS, 2021)

Generating Distributional Adversarial Examples to Evade Statistical Detectors. - Yigitcan Kaya; Muhammad Bilal Zafar; Sergül Aydöre; Nathalie Rauschmayr; Krishnaram Kenthapadi (ICML, 2022)

Adversarial Robustness of Learning-based Static Malware Classifiers. - Shoumik Saha; Wenxiao Wang 002; Yigitcan Kaya; Soheil Feizi (CoRR, 2023)

The Limitations of Deep Learning Methods in Realistic Adversarial Settings. - Yigitcan Kaya (2023)

Like Oil and Water: Group Robustness Methods and Poisoning Defenses May Be at Odds. - Michael-Andrei Panaitescu-Liess; Yigitcan Kaya; Sicheng Zhu; Furong Huang; Tudor Dumitras (ICLR, 2024)

DRSM: De-Randomized Smoothing on Malware Classifier Providing Certified Robustness. - Shoumik Saha; Wenxiao Wang 002; Yigitcan Kaya; Soheil Feizi; Tudor Dumitras (ICLR, 2024)

Understanding, Uncovering, and Mitigating the Causes of Inference Slowdown for Language Models. - Kamala Varma; Arda Numanoglu; Yigitcan Kaya; Tudor Dumitras (SaTML, 2024)

Demystifying Behavior-Based Malware Detection at Endpoints. - Yigitcan Kaya; Yizheng Chen 001; Shoumik Saha; Fabio Pierazzi; Lorenzo Cavallaro; David A. Wagner 001; Tudor Dumitras (CoRR, 2024)

PoisonedParrot: Subtle Data Poisoning Attacks to Elicit Copyright-Infringing Content from Large Language Models. - Michael-Andrei Panaitescu-Liess; Pankayaraj Pathmanathan; Yigitcan Kaya; Zora Che; Bang An 001; Sicheng Zhu; Aakriti Agrawal; Furong Huang (NAACL (Long Papers), 2025)

ML-Based Behavioral Malware Detection Is Far From a Solved Problem. - Yigitcan Kaya; Yizheng Chen 001; Marcus Botacin; Shoumik Saha; Fabio Pierazzi; Lorenzo Cavallaro; David A. Wagner 001; Tudor Dumitras (SaTML, 2025)

MADCAT: Combating Malware Detection Under Concept Drift with Test-Time Adaptation. - Eunjin Roh; Yigitcan Kaya; Christopher Kruegel; Giovanni Vigna; Sanghyun Hong 001 (CoRR, 2025)