103 citations · 412 across the 16 of their papers we have counts for
18 papers
Like Oil and Water: Group Robustness Methods and Poisoning Defenses May Be at Odds
Michael-Andrei Panaitescu-Liess, Yigitcan Kaya, Sicheng Zhu +2
Group robustness has become a major concern in machine learning (ML) as conventional training paradigms were found to produce high error on minority groups. Without explicit group…
ML-Based Behavioral Malware Detection Is Far From a Solved Problem
Yigitcan Kaya, Yizheng Chen, Marcus Botacin +5
Malware detection is a ubiquitous application of Machine Learning (ML) in security. In behavioral malware analysis, the detector relies on features extracted from program execution…
Qu-ANTI-zation: Exploiting Quantization Artifacts for Achieving Adversarial Outcomes
Sanghyun Hong, Michael-Andrei Panaitescu-Liess, Yiğitcan Kaya +1
Quantization is a popular technique that the parameter representation of a neural network from floating-point numbers into lower-precision ones (, 8-bit integers…
Technical Report -- Expected Exploitability: Predicting the Development of Functional Vulnerability Exploits
Octavian Suciu, Connor Nelson, Zhuoer Lyu +2
Assessing the exploitability of software vulnerabilities at the time of disclosure is difficult and error-prone, as features extracted via technical analysis by existing metrics ar…
A Panda? No, It's a Sloth: Slowdown Attacks on Adaptive Multi-Exit Neural Network Inference
Sanghyun Hong, Yiğitcan Kaya, Ionuţ-Vlad Modoranu +1
Recent increases in the computational demands of deep neural networks (DNNs), combined with the observation that most input samples require only simple models, have sparked interes…
On the Effectiveness of Regularization Against Membership Inference Attacks
Yigitcan Kaya, Sanghyun Hong, Tudor Dumitras
Deep learning models often raise privacy concerns as they leak information about their training data. This enables an adversary to determine whether a data point was in a model's t…