Enhancing Software Vulnerability Detection Using Code Property Graphs and Convolutional Neural Networks

• URL: http://arxiv.org/abs/2503.18175v1
• Date: Sun, 23 Mar 2025 19:12:07 GMT
• Title: Enhancing Software Vulnerability Detection Using Code Property Graphs and Convolutional Neural Networks
• Authors: Amanpreet Singh Saimbhi,
• Abstract summary: This paper proposes a novel approach to detecting software vulnerabilities using a combination of code property graphs and machine learning techniques.<n>We introduce various neural network models, including convolutional neural networks adapted for graph data, to process these representations.<n>Our contributions include a methodology for transforming software code into code property graphs, the implementation of a convolutional neural network model for graph data, and the creation of a comprehensive dataset for training and evaluation.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The increasing complexity of modern software systems has led to a rise in vulnerabilities that malicious actors can exploit. Traditional methods of vulnerability detection, such as static and dynamic analysis, have limitations in scalability and automation. This paper proposes a novel approach to detecting software vulnerabilities using a combination of code property graphs and machine learning techniques. By leveraging code property graphs, which integrate abstract syntax trees, control flow graphs, and program dependency graphs, we achieve a detailed representation of software code that enhances the accuracy and granularity of vulnerability detection. We introduce various neural network models, including convolutional neural networks adapted for graph data, to process these representations. Our approach provides a scalable and automated solution for vulnerability detection, addressing the shortcomings of existing methods. We also present a newly generated dataset labeled with function-level vulnerability types sourced from open-source repositories. Our contributions include a methodology for transforming software code into code property graphs, the implementation of a convolutional neural network model for graph data, and the creation of a comprehensive dataset for training and evaluation. This work lays the foundation for more effective and efficient vulnerability detection in complex software systems.

Related papers

• A Combined Feature Embedding Tools for Multi-Class Software Defect and Identification [2.2020053359163305]
  We present CodeGraphNet, an experimental method that combines GraphCodeBERT and Graph Convolutional Network approaches.<n>This method captures intricate relationships between features, providing for more exact identification and separation of vulnerabilities.<n>The DeepTree model, which is a hybrid of a Decision Tree and a Neural Network, outperforms state-of-the-art approaches.
  arXiv  Detail & Related papers  (2024-11-26T17:33:02Z)
• An Unbiased Transformer Source Code Learning with Semantic Vulnerability Graph [3.3598755777055374]
  Current vulnerability screening techniques are ineffective at identifying novel vulnerabilities or providing developers with code vulnerability and classification. To address these issues, we propose a joint multitasked unbiased vulnerability classifier comprising a transformer "RoBERTa" and graph convolution neural network (GCN) We present a training process utilizing a semantic vulnerability graph (SVG) representation from source code, created by integrating edges from a sequential flow, control flow, and data flow, as well as a novel flow dubbed Poacher Flow (PF)
  arXiv  Detail & Related papers  (2023-04-17T20:54:14Z)
• Improving robustness of jet tagging algorithms with adversarial training [56.79800815519762]
  We investigate the vulnerability of flavor tagging algorithms via application of adversarial attacks. We present an adversarial training strategy that mitigates the impact of such simulated attacks.
  arXiv  Detail & Related papers  (2022-03-25T19:57:19Z)
• Flurry: a Fast Framework for Reproducible Multi-layered Provenance Graph Representation Learning [0.44040106718326594]
  Flurry is an end-to-end data pipeline which simulates cyberattacks. It captures data from these attacks at multiple system and application layers, converts audit logs from these attacks into data provenance graphs, and incorporates this data with a framework for training deep neural models. We showcase this pipeline by processing data from multiple system attacks and performing anomaly detection via graph classification.
  arXiv  Detail & Related papers  (2022-03-05T13:52:11Z)
• VELVET: a noVel Ensemble Learning approach to automatically locate VulnErable sTatements [62.93814803258067]
  This paper presents VELVET, a novel ensemble learning approach to locate vulnerable statements in source code. Our model combines graph-based and sequence-based neural networks to successfully capture the local and global context of a program graph. VELVET achieves 99.6% and 43.6% top-1 accuracy over synthetic data and real-world data, respectively.
  arXiv  Detail & Related papers  (2021-12-20T22:45:27Z)
• ReGVD: Revisiting Graph Neural Networks for Vulnerability Detection [20.65271290295621]
  We propose ReGVD, a graph network-based model for vulnerability detection. In particular, ReGVD views a given source code as a flat sequence of tokens. We obtain the highest accuracy on the real-world benchmark dataset from CodeXGLUE for vulnerability detection.
  arXiv  Detail & Related papers  (2021-10-14T12:44:38Z)
• Software Vulnerability Detection via Deep Learning over Disaggregated Code Graph Representation [57.92972327649165]
  This work explores a deep learning approach to automatically learn the insecure patterns from code corpora. Because code naturally admits graph structures with parsing, we develop a novel graph neural network (GNN) to exploit both the semantic context and structural regularity of a program.
  arXiv  Detail & Related papers  (2021-09-07T21:24:36Z)
• Anomaly Detection on Attributed Networks via Contrastive Self-Supervised Learning [50.24174211654775]
  We present a novel contrastive self-supervised learning framework for anomaly detection on attributed networks. Our framework fully exploits the local information from network data by sampling a novel type of contrastive instance pair. A graph neural network-based contrastive learning model is proposed to learn informative embedding from high-dimensional attributes and local structure.
  arXiv  Detail & Related papers  (2021-02-27T03:17:20Z)
• Information Obfuscation of Graph Neural Networks [96.8421624921384]
  We study the problem of protecting sensitive attributes by information obfuscation when learning with graph structured data. We propose a framework to locally filter out pre-determined sensitive attributes via adversarial training with the total variation and the Wasserstein distance.
  arXiv  Detail & Related papers  (2020-09-28T17:55:04Z)
• Learning to map source code to software vulnerability using code-as-a-graph [67.62847721118142]
  We explore the applicability of Graph Neural Networks in learning the nuances of source code from a security perspective. We show that a code-as-graph encoding is more meaningful for vulnerability detection than existing code-as-photo and linear sequence encoding approaches.
  arXiv  Detail & Related papers  (2020-06-15T16:05:27Z)

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.