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Found 12 papers, 8 papers with code
Vulnerability Detection with Code Language Models: How Far Are We?
Evaluating code LMs on PrimeVul reveals that existing benchmarks significantly overestimate the performance of these models.
2L3: Lifting Imperfect Generated 2D Images into Accurate 3D
Specifically, we first leverage to decouple the shading information from the generated images to reduce the impact of inconsistent lighting; then, we introduce mono prior with view-dependent transient encoding to enhance the reconstructed normal; and finally, we design a view augmentation fusion strategy that minimizes pixel-level loss in generated sparse views and semantic loss in augmented random views, resulting in view-consistent geometry and detailed textures.
DiverseVul: A New Vulnerable Source Code Dataset for Deep Learning Based Vulnerability Detection
Combining our new dataset with previous datasets, we present an analysis of the challenges and promising research directions of using deep learning for detecting software vulnerabilities.
Continuous Learning for Android Malware Detection
We propose a new hierarchical contrastive learning scheme, and a new sample selection technique to continuously train the Android malware classifier.
Part-Based Models Improve Adversarial Robustness
We show that combining human prior knowledge with end-to-end learning can improve the robustness of deep neural networks by introducing a part-based model for object classification.
Learning Security Classifiers with Verified Global Robustness Properties
Since data distribution shift is very common in security applications, e. g., often observed for malware detection, local robustness cannot guarantee that the property holds for unseen inputs at the time of deploying the classifier.
Cost-Aware Robust Tree Ensembles for Security Applications
There are various costs for attackers to manipulate the features of security classifiers.
Neutaint: Efficient Dynamic Taint Analysis with Neural Networks
Dynamic taint analysis (DTA) is widely used by various applications to track information flow during runtime execution.
Cryptography and Security
Enhancing Gradient-based Attacks with Symbolic Intervals
In this paper, we present interval attacks, a new technique to find adversarial examples to evaluate the robustness of neural networks.
On Training Robust PDF Malware Classifiers
A practically useful malware classifier must be robust against evasion attacks.
MixTrain: Scalable Training of Verifiably Robust Neural Networks
Making neural networks robust against adversarial inputs has resulted in an arms race between new defenses and attacks.
Practical Attacks Against Graph-based Clustering
Graph modeling allows numerous security problems to be tackled in a general way, however, little work has been done to understand their ability to withstand adversarial attacks.
