Are We Learning the Right Features? A Framework for Evaluating DL-Based Software Vulnerability Detection Solutions

• URL: http://arxiv.org/abs/2501.13291v4
• Date: Thu, 13 Feb 2025 01:55:17 GMT
• Title: Are We Learning the Right Features? A Framework for Evaluating DL-Based Software Vulnerability Detection Solutions
• Authors: Satyaki Das, Syeda Tasnim Fabiha, Saad Shafiq, Nenad Medvidovic,
• Abstract summary: This paper aims to provide the foundation for properly evaluating the research in this domain. We analyze vulnerability datasets for the syntactic and semantic features of code that contribute to vulnerability. We provide a novel, uniform representation to capture both sets of features, and use this representation to detect the presence of both vulnerability and spurious features in code.
• Score: 3.204048014949849
• License:
• Abstract: Recent research has revealed that the reported results of an emerging body of DL-based techniques for detecting software vulnerabilities are not reproducible, either across different datasets or on unseen samples. This paper aims to provide the foundation for properly evaluating the research in this domain. We do so by analyzing prior work and existing vulnerability datasets for the syntactic and semantic features of code that contribute to vulnerability, as well as features that falsely correlate with vulnerability. We provide a novel, uniform representation to capture both sets of features, and use this representation to detect the presence of both vulnerability and spurious features in code. To this end, we design two types of code perturbations: feature preserving perturbations (FPP) ensure that the vulnerability feature remains in a given code sample, while feature eliminating perturbations (FEP) eliminate the feature from the code sample. These perturbations aim to measure the influence of spurious and vulnerability features on the predictions of a given vulnerability detection solution. To evaluate how the two classes of perturbations influence predictions, we conducted a large-scale empirical study on five state-of-the-art DL-based vulnerability detectors. Our study shows that, for vulnerability features, only ~2% of FPPs yield the undesirable effect of a prediction changing among the five detectors on average. However, on average, ~84% of FEPs yield the undesirable effect of retaining the vulnerability predictions. For spurious features, we observed that FPPs yielded a drop in recall up to 29% for graph-based detectors. We present the reasons underlying these results and suggest strategies for improving DNN-based vulnerability detectors. We provide our perturbation-based evaluation framework as a public resource to enable independent future evaluation of vulnerability detectors.

Related papers

• Similar but Patched Code Considered Harmful -- The Impact of Similar but Patched Code on Recurring Vulnerability Detection and How to Remove Them [8.404849985552776]
  We propose a programming language framework, Fixed Vulnerability Filter (FVF), to identify and filter such SBP instances in vulnerability detection. We apply FVF to 1,081 real-world software projects and construct a real-world SBP dataset containing 6,827 SBP functions.
  arXiv  Detail & Related papers  (2024-12-30T06:32:10Z)
• Deep Learning for Network Anomaly Detection under Data Contamination: Evaluating Robustness and Mitigating Performance Degradation [0.0]
  Deep learning (DL) has emerged as a crucial tool in network anomaly detection (NAD) for cybersecurity. While DL models for anomaly detection excel at extracting features and learning patterns from data, they are vulnerable to data contamination. This study evaluates the robustness of six unsupervised DL algorithms against data contamination.
  arXiv  Detail & Related papers  (2024-07-11T19:47:37Z)
• PASA: Attack Agnostic Unsupervised Adversarial Detection using Prediction & Attribution Sensitivity Analysis [2.5347892611213614]
  Deep neural networks for classification are vulnerable to adversarial attacks, where small perturbations to input samples lead to incorrect predictions. We develop a practical method for this characteristic of model prediction and feature attribution to detect adversarial samples. Our approach demonstrates competitive performance even when an adversary is aware of the defense mechanism.
  arXiv  Detail & Related papers  (2024-04-12T21:22:21Z)
• Vulnerability Detection with Code Language Models: How Far Are We? [40.455600722638906]
  PrimeVul is a new dataset for training and evaluating code LMs for vulnerability detection. It incorporates a novel set of data labeling techniques that achieve comparable label accuracy to human-verified benchmarks. It also implements a rigorous data de-duplication and chronological data splitting strategy to mitigate data leakage issues.
  arXiv  Detail & Related papers  (2024-03-27T14:34:29Z)
• Beyond Fidelity: Explaining Vulnerability Localization of Learning-based Detectors [10.316819421902363]
  Vulnerability detectors based on deep learning (DL) models have proven their effectiveness in recent years. The shroud of opacity surrounding the decision-making process of these detectors makes it difficult for security analysts to comprehend. We evaluate the performance of ten explanation approaches for vulnerability detectors based on graph and sequence representations.
  arXiv  Detail & Related papers  (2024-01-05T07:37:35Z)
• Conservative Prediction via Data-Driven Confidence Minimization [70.93946578046003]
  In safety-critical applications of machine learning, it is often desirable for a model to be conservative. We propose the Data-Driven Confidence Minimization framework, which minimizes confidence on an uncertainty dataset.
  arXiv  Detail & Related papers  (2023-06-08T07:05:36Z)
• Exploring Robustness of Unsupervised Domain Adaptation in Semantic Segmentation [74.05906222376608]
  We propose adversarial self-supervision UDA (or ASSUDA) that maximizes the agreement between clean images and their adversarial examples by a contrastive loss in the output space. This paper is rooted in two observations: (i) the robustness of UDA methods in semantic segmentation remains unexplored, which pose a security concern in this field; and (ii) although commonly used self-supervision (e.g., rotation and jigsaw) benefits image tasks such as classification and recognition, they fail to provide the critical supervision signals that could learn discriminative representation for segmentation tasks.
  arXiv  Detail & Related papers  (2021-05-23T01:50:44Z)
• WSSOD: A New Pipeline for Weakly- and Semi-Supervised Object Detection [75.80075054706079]
  We propose a weakly- and semi-supervised object detection framework (WSSOD) An agent detector is first trained on a joint dataset and then used to predict pseudo bounding boxes on weakly-annotated images. The proposed framework demonstrates remarkable performance on PASCAL-VOC and MSCOCO benchmark, achieving a high performance comparable to those obtained in fully-supervised settings.
  arXiv  Detail & Related papers  (2021-05-21T11:58:50Z)
• Exploiting Sample Uncertainty for Domain Adaptive Person Re-Identification [137.9939571408506]
  We estimate and exploit the credibility of the assigned pseudo-label of each sample to alleviate the influence of noisy labels. Our uncertainty-guided optimization brings significant improvement and achieves the state-of-the-art performance on benchmark datasets.
  arXiv  Detail & Related papers  (2020-12-16T04:09:04Z)
• Accurate and Robust Feature Importance Estimation under Distribution Shifts [49.58991359544005]
  PRoFILE is a novel feature importance estimation method. We show significant improvements over state-of-the-art approaches, both in terms of fidelity and robustness.
  arXiv  Detail & Related papers  (2020-09-30T05:29:01Z)
• Unlabelled Data Improves Bayesian Uncertainty Calibration under Covariate Shift [100.52588638477862]
  We develop an approximate Bayesian inference scheme based on posterior regularisation. We demonstrate the utility of our method in the context of transferring prognostic models of prostate cancer across globally diverse populations.
  arXiv  Detail & Related papers  (2020-06-26T13:50:19Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.