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08/30/2023
Threshold KNN-Shapley: A Linear-Time and Privacy-Friendly Approach to Data Valuation
Data valuation, a critical aspect of data-centric ML research, aims to q...
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08/23/2023
BaDExpert: Extracting Backdoor Functionality for Accurate Backdoor Input Detection
We present a novel defense, against backdoor attacks on Deep Neural Netw...
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08/03/2023
Food Classification using Joint Representation of Visual and Textual Data
Food classification is an important task in health care. In this work, w...
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06/22/2023
Visual Adversarial Examples Jailbreak Large Language Models
Recently, there has been a surge of interest in introducing vision into ...
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06/08/2023
Differentially Private Image Classification by Learning Priors from Random Processes
In privacy-preserving machine learning, differentially private stochasti...
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05/02/2023
Differentially Private In-Context Learning
An important question in deploying large language models (LLMs) is how t...
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02/21/2023
Characterizing the Optimal 0-1 Loss for Multi-class Classification with a Test-time Attacker
Finding classifiers robust to adversarial examples is critical for their...
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02/03/2023
Augmenting Rule-based DNS Censorship Detection at Scale with Machine Learning
The proliferation of global censorship has led to the development of a p...
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09/15/2022
A Light Recipe to Train Robust Vision Transformers
In this paper, we ask whether Vision Transformers (ViTs) can serve as an...
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06/20/2022
Understanding Robust Learning through the Lens of Representation Similarities
Representation learning, i.e. the generation of representations useful f...
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06/12/2022
Neurotoxin: Durable Backdoors in Federated Learning
Due to their decentralized nature, federated learning (FL) systems have ...
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03/23/2022
Towards Reproducible Network Traffic Analysis
Analysis techniques are critical for gaining insight into network traffi...
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02/18/2022
Reviews in motion: a large scale, longitudinal study of review recommendations on Yelp
The United Nations Consumer Protection Guidelines lists "access ... to a...
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10/15/2021
Mitigating Membership Inference Attacks by Self-Distillation Through a Novel Ensemble Architecture
Membership inference attacks are a key measure to evaluate privacy leaka...
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10/11/2021
Parameterizing Activation Functions for Adversarial Robustness
Deep neural networks are known to be vulnerable to adversarially perturb...
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08/20/2021
PatchCleanser: Certifiably Robust Defense against Adversarial Patches for Any Image Classifier
The adversarial patch attack against image classification models aims to...
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08/19/2021
F-PKI: Enabling Innovation and Trust Flexibility in the HTTPS Public-Key Infrastructure
We present F-PKI, an enhancement to the HTTPS public-key infrastructure ...
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04/26/2021
PatchGuard++: Efficient Provable Attack Detection against Adversarial Patches
An adversarial patch can arbitrarily manipulate image pixels within a re...
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04/19/2021
Improving Adversarial Robustness Using Proxy Distributions
We focus on the use of proxy distributions, i.e., approximations of the ...
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04/16/2021
Lower Bounds on Cross-Entropy Loss in the Presence of Test-time Adversaries
Understanding the fundamental limits of robust supervised learning has e...
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03/22/2021
SSD: A Unified Framework for Self-Supervised Outlier Detection
We ask the following question: what training information is required to ...
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02/05/2021
DetectorGuard: Provably Securing Object Detectors against Localized Patch Hiding Attacks
State-of-the-art object detectors are vulnerable to localized patch hidi...
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01/17/2021
A System for Efficiently Hunting for Cyber Threats in Computer Systems Using Threat Intelligence
Log-based cyber threat hunting has emerged as an important solution to c...
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10/26/2020
Enabling Efficient Cyber Threat Hunting With Cyber Threat Intelligence
Log-based cyber threat hunting has emerged as an important solution to c...
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10/19/2020
RobustBench: a standardized adversarial robustness benchmark
Evaluation of adversarial robustness is often error-prone leading to ove...
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08/06/2020
nPrint: A Standard Data Representation for Network Traffic Analysis
Conventional detection and classification ("fingerprinting") problems in...
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07/23/2020
Evaluating Snowflake as an Indistinguishable Censorship Circumvention Tool
Tor is the most well-known tool for circumventing censorship. Unfortunat...
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07/08/2020
A Critical Evaluation of Open-World Machine Learning
Open-world machine learning (ML) combines closed-world models trained on...
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06/24/2020
Time for a Background Check! Uncovering the impact of Background Features on Deep Neural Networks
With increasing expressive power, deep neural networks have significantl...
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05/29/2020
Programmable In-Network Obfuscation of Traffic
Recent advances in programmable switch hardware offer a fresh opportunit...
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05/17/2020
PatchGuard: Provable Defense against Adversarial Patches Using Masks on Small Receptive Fields
Localized adversarial patches aim to induce misclassification in machine...
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04/20/2020
Securing Internet Applications from Routing Attacks
Attacks on Internet routing are typically viewed through the lens of ava...
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04/19/2020
DP-Cryptography: Marrying Differential Privacy and Cryptography in Emerging Applications
Differential privacy (DP) has arisen as the state-of-the-art metric for ...
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04/05/2020
FALCON: Honest-Majority Maliciously Secure Framework for Private Deep Learning
This paper aims to enable training and inference of neural networks in a...
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03/24/2020
Systematic Evaluation of Privacy Risks of Machine Learning Models
Machine learning models are prone to memorizing sensitive data, making t...
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03/09/2020
Towards Probabilistic Verification of Machine Unlearning
Right to be forgotten, also known as the right to erasure, is the right ...
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02/24/2020
On Pruning Adversarially Robust Neural Networks
In safety-critical but computationally resource-constrained applications...
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12/25/2019
Grand Challenges of Resilience: Autonomous System Resilience through Design and Runtime Measures
A set of about 80 researchers, practitioners, and federal agency program...
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12/10/2019
Advances and Open Problems in Federated Learning
Federated learning (FL) is a machine learning setting where many clients...
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09/26/2019
Lower Bounds on Adversarial Robustness from Optimal Transport
While progress has been made in understanding the robustness of machine ...
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06/14/2019
Towards Compact and Robust Deep Neural Networks
Deep neural networks have achieved impressive performance in many applic...
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05/24/2019
Privacy Risks of Securing Machine Learning Models against Adversarial Examples
The arms race between attacks and defenses for machine learning models h...
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05/05/2019
Better the Devil you Know: An Analysis of Evasion Attacks using Out-of-Distribution Adversarial Examples
A large body of recent work has investigated the phenomenon of evasion a...
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03/19/2019
A Stream-based Query System for Efficiently Detecting Abnormal System Behaviors for Enterprise Security
The need for countering Advanced Persistent Threat (APT) attacks has led...
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11/29/2018
Analyzing Federated Learning through an Adversarial Lens
Federated learning distributes model training among a multitude of agent...
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11/17/2018
Robust Website Fingerprinting Through the Cache Occupancy Channel
Website fingerprinting attacks, which use statistical analysis on networ...
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10/04/2018
A Query Tool for Efficiently Investigating Risky Software Behaviors
Advanced Persistent Threat (APT) attacks are sophisticated and stealthy,...
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09/10/2018
Partial Recovery of Erdős-Rényi Graph Alignment via k-Core Alignment
We determine information theoretic conditions under which it is possible...
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06/25/2018
SAQL: A Stream-based Query System for Real-Time Abnormal System Behavior Detection
Recently, advanced cyber attacks, which consist of a sequence of steps t...
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06/06/2018
