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04/01/2023
DiverseVul: A New Vulnerable Source Code Dataset for Deep Learning Based Vulnerability Detection
We propose and release a new vulnerable source code dataset. We curate t...
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02/08/2023
Continuous Learning for Android Malware Detection
Machine learning methods can detect Android malware with very high accur...
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01/12/2022
Diffix Elm: Simple Diffix
Historically, strong data anonymization requires substantial domain expe...
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12/23/2021
SLIP: Self-supervision meets Language-Image Pre-training
Recent work has shown that self-supervised pre-training leads to improve...
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05/27/2021
Hopper: Modeling and Detecting Lateral Movement (Extended Report)
In successful enterprise attacks, adversaries often need to gain access ...
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05/24/2021
Learning Security Classifiers with Verified Global Robustness Properties
Recent works have proposed methods to train classifiers with local robus...
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05/18/2021
Fighting Gradients with Gradients: Dynamic Defenses against Adversarial Attacks
Adversarial attacks optimize against models to defeat defenses. Existing...
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03/01/2021
Model-Agnostic Defense for Lane Detection against Adversarial Attack
Susceptibility of neural networks to adversarial attack prompts serious ...
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11/19/2020
Adversarial Examples for k-Nearest Neighbor Classifiers Based on Higher-Order Voronoi Diagrams
Adversarial examples are a widely studied phenomenon in machine learning...
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07/28/2020
A Large-Scale Analysis of Attacker Activity in Compromised Enterprise Accounts
We present a large-scale characterization of attacker activity across 11...
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04/28/2020
Minority Reports Defense: Defending Against Adversarial Patches
Deep learning image classification is vulnerable to adversarial attack, ...
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03/18/2020
Improving Adversarial Robustness Through Progressive Hardening
Adversarial training (AT) has become a popular choice for training robus...
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03/14/2020
Minimum-Norm Adversarial Examples on KNN and KNN-Based Models
We study the robustness against adversarial examples of kNN classifiers ...
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10/02/2019
Detecting and Characterizing Lateral Phishing at Scale
We present the first large-scale characterization of lateral phishing at...
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07/12/2019
Stateful Detection of Black-Box Adversarial Attacks
The problem of adversarial examples, evasion attacks on machine learning...
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06/23/2019
Defending Against Adversarial Examples with K-Nearest Neighbor
Robustness is an increasingly important property of machine learning mod...
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03/20/2019
On the Robustness of Deep K-Nearest Neighbors
Despite a large amount of attention on adversarial examples, very few wo...
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02/01/2018
Obfuscated Gradients Give a False Sense of Security: Circumventing Defenses to Adversarial Examples
We identify obfuscated gradients as a phenomenon that leads to a false s...
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01/05/2018
Audio Adversarial Examples: Targeted Attacks on Speech-to-Text
We construct targeted audio adversarial examples on automatic speech rec...
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11/22/2017
MagNet and "Efficient Defenses Against Adversarial Attacks" are Not Robust to Adversarial Examples
MagNet and "Efficient Defenses..." were recently proposed as a defense t...
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05/20/2017
Adversarial Examples Are Not Easily Detected: Bypassing Ten Detection Methods
Neural networks are known to be vulnerable to adversarial examples: inpu...
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08/16/2016
Towards Evaluating the Robustness of Neural Networks
Neural networks provide state-of-the-art results for most machine learni...
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08/06/2016
Spoofing 2D Face Detection: Machines See People Who Aren't There
Machine learning is increasingly used to make sense of the physical worl...
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07/14/2016
