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09/19/2023
The Impact of Exposed Passwords on Honeyword Efficacy
Honeywords are decoy passwords that can be added to a credential databas...
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06/29/2023
Group-based Robustness: A General Framework for Customized Robustness in the Real World
Machine-learning models are known to be vulnerable to evasion attacks th...
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02/27/2023
Randomness in ML Defenses Helps Persistent Attackers and Hinders Evaluators
It is becoming increasingly imperative to design robust ML defenses. How...
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01/31/2023
Certified Robustness of Learning-based Static Malware Detectors
Certified defenses are a recent development in adversarial machine learn...
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12/28/2021
Constrained Gradient Descent: A Powerful and Principled Evasion Attack Against Neural Networks
Minimal adversarial perturbations added to inputs have been shown to be ...
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10/19/2020
(How) Do people change their passwords after a breach?
To protect against misuse of passwords compromised in a breach, consumer...
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10/19/2020
What breach? Measuring online awareness of security incidents by studying real-world browsing behavior
Awareness about security and privacy risks is important for developing g...
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12/19/2019
Optimization-Guided Binary Diversification to Mislead Neural Networks for Malware Detection
Motivated by the transformative impact of deep neural networks (DNNs) on...
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12/19/2019
n-ML: Mitigating Adversarial Examples via Ensembles of Topologically Manipulated Classifiers
This paper proposes a new defense called n-ML against adversarial exampl...
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02/27/2018
On the Suitability of L_p-norms for Creating and Preventing Adversarial Examples
Much research effort has been devoted to better understanding adversaria...
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12/31/2017
