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08/07/2023
Symmetry-Preserving Program Representations for Learning Code Semantics
Large Language Models (LLMs) have shown promise in automated program rea...
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10/04/2022
NeuDep: Neural Binary Memory Dependence Analysis
Determining whether multiple instructions can access the same memory loc...
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08/30/2022
MC^2: Rigorous and Efficient Directed Greybox Fuzzing
Directed greybox fuzzing is a popular technique for targeted software te...
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08/11/2022
General Cutting Planes for Bound-Propagation-Based Neural Network Verification
Bound propagation methods, when combined with branch and bound, are amon...
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03/22/2022
Effective Seed Scheduling for Fuzzing with Graph Centrality Analysis
Seed scheduling, the order in which seeds are selected, can greatly affe...
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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...
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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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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...
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12/16/2020
Trex: Learning Execution Semantics from Micro-Traces for Binary Similarity
Detecting semantically similar functions – a crucial analysis capability...
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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...
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10/02/2020
XDA: Accurate, Robust Disassembly with Transfer Learning
Accurate and robust disassembly of stripped binaries is challenging. The...
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07/12/2020
Ensuring Fairness Beyond the Training Data
We initiate the study of fair classifiers that are robust to perturbatio...
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06/04/2020
Towards Understanding Fast Adversarial Training
Current neural-network-based classifiers are susceptible to adversarial ...
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05/25/2020
MTFuzz: Fuzzing with a Multi-Task Neural Network
Fuzzing is a widely used technique for detecting software bugs and vulne...
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03/17/2020
Learning Nonlinear Loop Invariants with Gated Continuous Logic Networks (Extended Version)
Verifying real-world programs often requires inferring loop invariants w...
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03/17/2020
Learning Nonlinear Loop Invariants with Gated Continuous Logic Networks
In many cases, verifying real-world programs requires inferring loop inv...
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03/06/2020
Towards Practical Lottery Ticket Hypothesis for Adversarial Training
Recent research has proposed the lottery ticket hypothesis, suggesting t...
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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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12/20/2019
Resilient Cyberphysical Systems and their Application Drivers: A Technology Roadmap
Cyberphysical systems (CPS) are ubiquitous in our personal and professio...
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12/03/2019
Training Robust Tree Ensembles for Security
Tree ensemble models including random forests and gradient boosted decis...
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09/25/2019
CLN2INV: Learning Loop Invariants with Continuous Logic Networks
Program verification offers a framework for ensuring program correctness...
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09/08/2019
Fine Grained Dataflow Tracking with Proximal Gradients
Dataflow tracking with Dynamic Taint Analysis (DTA) is an important meth...
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07/08/2019
Neutaint: Efficient Dynamic Taint Analysis with Neural Networks
Dynamic taint analysis (DTA) is widely used by various applications to t...
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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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06/05/2019
Enhancing Gradient-based Attacks with Symbolic Intervals
Recent breakthroughs in defenses against adversarial examples, like adve...
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04/06/2019
On Training Robust PDF Malware Classifiers
Although state-of-the-art PDF malware classifiers can be trained with al...
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11/06/2018
MixTrain: Scalable Training of Verifiably Robust Neural Networks
Making neural networks robust against adversarial inputs has resulted in...
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11/06/2018
MixTrain: Scalable Training of Formally Robust Neural Networks
There is an arms race to defend neural networks against adversarial exam...
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09/19/2018
Efficient Formal Safety Analysis of Neural Networks
Neural networks are increasingly deployed in real-world safety-critical ...
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07/15/2018
NEUZZ: Efficient Fuzzing with Neural Program Learning
Fuzzing has become the de facto standard technique for finding software ...
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04/28/2018
Formal Security Analysis of Neural Networks using Symbolic Intervals
Due to the increasing deployment of Deep Neural Networks (DNNs) in real-...
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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...
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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...
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08/29/2017
Tug-of-War: Observations on Unified Content Handling
Modern applications and Operating Systems vary greatly with respect to h...
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08/28/2017
