b'Suman Jana'

research

∙ 08/07/2023

Symmetry-Preserving Program Representations for Learning Code Semantics

Large Language Models (LLMs) have shown promise in automated program rea...

0 Kexin Pei, et al. ∙

research

∙ 10/04/2022

NeuDep: Neural Binary Memory Dependence Analysis

Determining whether multiple instructions can access the same memory loc...

0 Kexin Pei, et al. ∙

research

∙ 08/30/2022

MC^2: Rigorous and Efficient Directed Greybox Fuzzing

Directed greybox fuzzing is a popular technique for targeted software te...

0 Abhishek Shah, et al. ∙

research

∙ 08/11/2022

General Cutting Planes for Bound-Propagation-Based Neural Network Verification

Bound propagation methods, when combined with branch and bound, are amon...

7 Huan Zhang, et al. ∙

research

∙ 03/22/2022

Effective Seed Scheduling for Fuzzing with Graph Centrality Analysis

Seed scheduling, the order in which seeds are selected, can greatly affe...

0 Dongdong She, et al. ∙

research

∙ 06/18/2021

Evaluating the Robustness of Trigger Set-Based Watermarks Embedded in Deep Neural Networks

Trigger set-based watermarking schemes have gained emerging attention as...

0 Suyoung Lee, et al. ∙

research

∙ 05/24/2021

Learning Security Classifiers with Verified Global Robustness Properties

Recent works have proposed methods to train classifiers with local robus...

5 Yizheng Chen, et al. ∙

research

∙ 03/11/2021

Beta-CROWN: Efficient Bound Propagation with Per-neuron Split Constraints for Complete and Incomplete Neural Network Verification

Recent works in neural network verification show that cheap incomplete v...

0 Shiqi Wang, et al. ∙

research

∙ 12/16/2020

Trex: Learning Execution Semantics from Micro-Traces for Binary Similarity

Detecting semantically similar functions – a crucial analysis capability...

0 Kexin Pei, et al. ∙

research

∙ 11/27/2020

Fast and Complete: Enabling Complete Neural Network Verification with Rapid and Massively Parallel Incomplete Verifiers

Formal verification of neural networks (NNs) is a challenging and import...

0 Kaidi Xu, et al. ∙

research

∙ 10/02/2020

XDA: Accurate, Robust Disassembly with Transfer Learning

Accurate and robust disassembly of stripped binaries is challenging. The...

0 Kexin Pei, et al. ∙

research

∙ 07/12/2020

Ensuring Fairness Beyond the Training Data

We initiate the study of fair classifiers that are robust to perturbatio...

49 Debmalya Mandal, et al. ∙

research

∙ 06/04/2020

Towards Understanding Fast Adversarial Training

Current neural-network-based classifiers are susceptible to adversarial ...

17 Bai Li, et al. ∙

research

∙ 05/25/2020

MTFuzz: Fuzzing with a Multi-Task Neural Network

Fuzzing is a widely used technique for detecting software bugs and vulne...

0 Dongdong She, et al. ∙

research

∙ 03/17/2020

Learning Nonlinear Loop Invariants with Gated Continuous Logic Networks (Extended Version)

Verifying real-world programs often requires inferring loop invariants w...

0 Jianan Yao, et al. ∙

research

∙ 03/17/2020

Learning Nonlinear Loop Invariants with Gated Continuous Logic Networks

In many cases, verifying real-world programs requires inferring loop inv...

0 Jianan Yao, et al. ∙

research

∙ 03/06/2020

Towards Practical Lottery Ticket Hypothesis for Adversarial Training

Recent research has proposed the lottery ticket hypothesis, suggesting t...

2 Bai Li, et al. ∙

research

∙ 02/24/2020

On Pruning Adversarially Robust Neural Networks

In safety-critical but computationally resource-constrained applications...

0 Vikash Sehwag, et al. ∙

research

∙ 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...

0 Saurabh Bagchi, et al. ∙

research

∙ 12/20/2019

Resilient Cyberphysical Systems and their Application Drivers: A Technology Roadmap

Cyberphysical systems (CPS) are ubiquitous in our personal and professio...

0 Somali Chaterji, et al. ∙

research

∙ 12/03/2019

Training Robust Tree Ensembles for Security

Tree ensemble models including random forests and gradient boosted decis...

0 Yizheng Chen, et al. ∙

research

∙ 09/25/2019

CLN2INV: Learning Loop Invariants with Continuous Logic Networks

Program verification offers a framework for ensuring program correctness...

0 Gabriel Ryan, et al. ∙

research

∙ 09/08/2019

Fine Grained Dataflow Tracking with Proximal Gradients

Dataflow tracking with Dynamic Taint Analysis (DTA) is an important meth...

0 Gabriel Ryan, et al. ∙

research

∙ 07/08/2019

Neutaint: Efficient Dynamic Taint Analysis with Neural Networks

Dynamic taint analysis (DTA) is widely used by various applications to t...

0 Dongdong She, et al. ∙

research

∙ 06/14/2019

Towards Compact and Robust Deep Neural Networks

Deep neural networks have achieved impressive performance in many applic...

0 Vikash Sehwag, et al. ∙

research

∙ 06/05/2019

Enhancing Gradient-based Attacks with Symbolic Intervals

Recent breakthroughs in defenses against adversarial examples, like adve...

0 Shiqi Wang, et al. ∙

research

∙ 04/06/2019

On Training Robust PDF Malware Classifiers

Although state-of-the-art PDF malware classifiers can be trained with al...

0 Yizheng Chen, et al. ∙

research

∙ 11/06/2018

MixTrain: Scalable Training of Verifiably Robust Neural Networks

Making neural networks robust against adversarial inputs has resulted in...

1 Shiqi Wang, et al. ∙

research

∙ 11/06/2018

MixTrain: Scalable Training of Formally Robust Neural Networks

There is an arms race to defend neural networks against adversarial exam...

0 Shiqi Wang, et al. ∙

research

∙ 09/19/2018

Efficient Formal Safety Analysis of Neural Networks

Neural networks are increasingly deployed in real-world safety-critical ...

0 Shiqi Wang, et al. ∙

research

∙ 07/15/2018

NEUZZ: Efficient Fuzzing with Neural Program Learning

Fuzzing has become the de facto standard technique for finding software ...

0 Dongdong She, et al. ∙

research

∙ 04/28/2018

Formal Security Analysis of Neural Networks using Symbolic Intervals

Due to the increasing deployment of Deep Neural Networks (DNNs) in real-...

0 Shiqi Wang, et al. ∙

research

∙ 02/09/2018

On the Connection between Differential Privacy and Adversarial Robustness in Machine Learning

Adversarial examples in machine learning has been a topic of intense res...

0 Mathias Lecuyer, et al. ∙

research

∙ 12/05/2017

Towards Practical Verification of Machine Learning: The Case of Computer Vision Systems

Due to the increasing usage of machine learning (ML) techniques in secur...

0 Kexin Pei, et al. ∙

research

∙ 08/29/2017

Tug-of-War: Observations on Unified Content Handling

Modern applications and Operating Systems vary greatly with respect to h...

0 Theofilos Petsios, et al. ∙

research

∙ 08/28/2017

DeepTest: Automated Testing of Deep-Neural-Network-driven Autonomous Cars

Recent advances in Deep Neural Networks (DNNs) have led to the developme...

0 Yuchi Tian, et al. ∙

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