V2W-BERT: A Framework for Effective Hierarchical Multiclass Classification of Software Vulnerabilities

• URL: http://arxiv.org/abs/2102.11498v1
• Date: Tue, 23 Feb 2021 05:16:57 GMT
• Title: V2W-BERT: A Framework for Effective Hierarchical Multiclass Classification of Software Vulnerabilities
• Authors: Siddhartha Shankar Das, Edoardo Serra, Mahantesh Halappanavar, Alex Pothen, Ehab Al-Shaer
• Abstract summary: We present a novel Transformer-based learning framework (V2W-BERT) in this paper. By using ideas from natural language processing, link prediction and transfer learning, our method outperforms previous approaches. We achieve up to 97% prediction accuracy for randomly partitioned data and up to 94% prediction accuracy in temporally partitioned data.
• Score: 7.906207218788341
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Weaknesses in computer systems such as faults, bugs and errors in the architecture, design or implementation of software provide vulnerabilities that can be exploited by attackers to compromise the security of a system. Common Weakness Enumerations (CWE) are a hierarchically designed dictionary of software weaknesses that provide a means to understand software flaws, potential impact of their exploitation, and means to mitigate these flaws. Common Vulnerabilities and Exposures (CVE) are brief low-level descriptions that uniquely identify vulnerabilities in a specific product or protocol. Classifying or mapping of CVEs to CWEs provides a means to understand the impact and mitigate the vulnerabilities. Since manual mapping of CVEs is not a viable option, automated approaches are desirable but challenging. We present a novel Transformer-based learning framework (V2W-BERT) in this paper. By using ideas from natural language processing, link prediction and transfer learning, our method outperforms previous approaches not only for CWE instances with abundant data to train, but also rare CWE classes with little or no data to train. Our approach also shows significant improvements in using historical data to predict links for future instances of CVEs, and therefore, provides a viable approach for practical applications. Using data from MITRE and National Vulnerability Database, we achieve up to 97% prediction accuracy for randomly partitioned data and up to 94% prediction accuracy in temporally partitioned data. We believe that our work will influence the design of better methods and training models, as well as applications to solve increasingly harder problems in cybersecurity.

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. This method captures intricate relation- ships between features, providing for more exact identification and separation of vulnerabilities. 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)
• CRepair: CVAE-based Automatic Vulnerability Repair Technology [1.147605955490786]
  Software vulnerabilities pose significant threats to the integrity, security, and reliability of modern software and its application data. To address the challenges of vulnerability repair, researchers have proposed various solutions, with learning-based automatic vulnerability repair techniques gaining widespread attention. This paper proposes CRepair, a CVAE-based automatic vulnerability repair technology aimed at fixing security vulnerabilities in system code.
  arXiv  Detail & Related papers  (2024-11-08T12:55:04Z)
• CTINEXUS: Leveraging Optimized LLM In-Context Learning for Constructing Cybersecurity Knowledge Graphs Under Data Scarcity [49.657358248788945]
  Textual descriptions in cyber threat intelligence (CTI) reports are rich sources of knowledge about cyber threats. Current CTI extraction methods lack flexibility and generalizability, often resulting in inaccurate and incomplete knowledge extraction. We propose CTINexus, a novel framework leveraging optimized in-context learning (ICL) of large language models.
  arXiv  Detail & Related papers  (2024-10-28T14:18:32Z)
• DFEPT: Data Flow Embedding for Enhancing Pre-Trained Model Based Vulnerability Detection [7.802093464108404]
  We propose a data flow embedding technique to enhance the performance of pre-trained models in vulnerability detection tasks. Specifically, we parse data flow graphs from function-level source code, and use the data type of the variable as the node characteristics of the DFG. Our research shows that DFEPT can provide effective vulnerability semantic information to pre-trained models, achieving an accuracy of 64.97% on the Devign dataset and an F1-Score of 47.9% on the Reveal dataset.
  arXiv  Detail & Related papers  (2024-10-24T07:05:07Z)
• Enhancing Pre-Trained Language Models for Vulnerability Detection via Semantic-Preserving Data Augmentation [4.374800396968465]
  We propose a data augmentation technique aimed at enhancing the performance of pre-trained language models for vulnerability detection. By incorporating our augmented dataset in fine-tuning a series of representative code pre-trained models, up to 10.1% increase in accuracy and 23.6% increase in F1 can be achieved.
  arXiv  Detail & Related papers  (2024-09-30T21:44:05Z)
• HW-V2W-Map: Hardware Vulnerability to Weakness Mapping Framework for Root Cause Analysis with GPT-assisted Mitigation Suggestion [3.847218857469107]
  We presentHW-V2W-Map Framework, which is a Machine Learning (ML) framework focusing on hardware vulnerabilities and Internet of Things (IoT) security. The architecture that we have proposed incorporates an Ontology-driven Storytelling framework, which automates the process of updating the Ontology. Our proposed framework utilized Generative Pre-trained Transformer (GPT) Large Language Models (LLMs) to provide mitigation suggestions.
  arXiv  Detail & Related papers  (2023-12-21T02:14:41Z)
• Enhancing Multiple Reliability Measures via Nuisance-extended Information Bottleneck [77.37409441129995]
  In practical scenarios where training data is limited, many predictive signals in the data can be rather from some biases in data acquisition. We consider an adversarial threat model under a mutual information constraint to cover a wider class of perturbations in training. We propose an autoencoder-based training to implement the objective, as well as practical encoder designs to facilitate the proposed hybrid discriminative-generative training.
  arXiv  Detail & Related papers  (2023-03-24T16:03:21Z)
• 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)
• RoFL: Attestable Robustness for Secure Federated Learning [59.63865074749391]
  Federated Learning allows a large number of clients to train a joint model without the need to share their private data. To ensure the confidentiality of the client updates, Federated Learning systems employ secure aggregation. We present RoFL, a secure Federated Learning system that improves robustness against malicious clients.
  arXiv  Detail & Related papers  (2021-07-07T15:42:49Z)
• Federated Learning with Unreliable Clients: Performance Analysis and Mechanism Design [76.29738151117583]
  Federated Learning (FL) has become a promising tool for training effective machine learning models among distributed clients. However, low quality models could be uploaded to the aggregator server by unreliable clients, leading to a degradation or even a collapse of training. We model these unreliable behaviors of clients and propose a defensive mechanism to mitigate such a security risk.
  arXiv  Detail & Related papers  (2021-05-10T08:02:27Z)
• Robust and Transferable Anomaly Detection in Log Data using Pre-Trained Language Models [59.04636530383049]
  Anomalies or failures in large computer systems, such as the cloud, have an impact on a large number of users. We propose a framework for anomaly detection in log data, as a major troubleshooting source of system information.
  arXiv  Detail & Related papers  (2021-02-23T09:17:05Z)

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.