Research CWE Mapping tools

[Crashedmind]

https://essay.utwente.nl/104547/ In this paper, we propose a new method to re-classify weaknesses in vulnerabilities with more labels. We do so through a classification model that generates an abundance of relevant weakness labels, in our experiments this abundance of labels already create significant F1-score improvement, however with a lower precision. We then use these generated labels as suggestions for experts to review, generating a new set of expert curated labels. These curated labels then become new dataset labels and are used retraining our classification model thresholds. Our experiments demonstrate that utilizing even small datasets of expert evaluations can lead to a significant precision improvement while maintaining a similar F1-score compared abundance of labels.

chatgpt search prompt: automation for cve cwe

Automating the mapping of Common Vulnerabilities and Exposures (CVEs) to Common Weakness Enumerations (CWEs) is crucial for efficient vulnerability management. This process enables organizations to swiftly identify underlying software weaknesses and implement appropriate mitigations. Several methodologies and tools have been developed to facilitate this automation:

1. Hierarchical Neural Networks: The ThreatZoom tool employs an adaptive hierarchical neural network to classify CVEs into their corresponding CWE classes. By analyzing both statistical and semantic features from CVE descriptions, ThreatZoom achieves classification accuracies of 92% (fine-grain) and 94% (coarse-grain) on the National Vulnerability Database (NVD) dataset. ARXIV https://arxiv.org/abs/2009.11501

2. Transformer-Based Models: The V2W-BERT framework utilizes transformer-based learning to map CVEs to CWEs effectively. Leveraging natural language processing and transfer learning, V2W-BERT attains up to 97% prediction accuracy on randomly partitioned data and 94% on temporally partitioned data. ARXIV https://arxiv.org/abs/2102.11498

3. Sequence-to-Sequence Learning: SeqTrans applies sequence-to-sequence learning models to automatically generate fixes for vulnerabilities. By incorporating data flow dependencies into code sequences, SeqTrans achieves a 23.3% accuracy in statement-level fixes and 25.3% in CVE-level fixes. ARXIV https://arxiv.org/abs/2010.10805

4. CVE Drill: CVE Drill is a predictive model that automates the classification of CVEs into CWE classes and estimates the Common Vulnerability Scoring System (CVSS) vector for precise threat mitigation. This tool enhances the accuracy and timeliness of cybersecurity countermeasures. ARXIV https://github.com/center-for-threat-informed-defense/cwe-calculator/

5. CWE with Environmental CVSS Calculator: This tool computes an average CVSS score for the CVEs associated with a given CWE, providing an estimate of its severity. It utilizes data from the NVD and allows customization through timeboxing, CWE normalization, and the application of CVSS environmental modifiers. GITHUB https://github.com/center-for-threat-informed-defense/cwe-calculator/

chatgpt search prompt: these are great. can you list 10 more such examples?

Certainly, here are ten tools and methodologies that automate the mapping of Common Vulnerabilities and Exposures (CVEs) to Common Weakness Enumerations (CWEs):

1. VulnScopper: Utilizes multi-modal representation learning by combining Knowledge Graphs (KG) and Natural Language Processing (NLP) to automate and enhance the analysis of software vulnerabilities. It effectively handles unseen entities, improving link prediction accuracy between CVEs, CWEs, and Common Platform Enumerations (CPEs).

2. CVE2CWE: An automated approach that leverages natural language processing techniques to extract relevant information from CVE descriptions and map them to corresponding CWEs. It utilizes TF-IDF vector representations to model CWEs and CVEs, facilitating accurate and efficient mapping.

3. OWASP CWE Toolkit: Provides code SDKs and tools to access CWE data programmatically, enabling various automations such as filtering security vulnerabilities (CVEs) based on their CWE identifier, relationships, or string-matched expressions.

4. CVE2CWE Mapping Tool: Developed by the Center for Threat-Informed Defense, this tool maps CVEs to CWEs to help organizations understand the weaknesses that lead to vulnerabilities and prioritize remediation efforts.

5. Automated CVE Analysis for Threat Prioritization and Impact Prediction: Introduces a predictive model and tool called CVEDrill, which automates the classification of CVEs into CWE classes and estimates the CVSS vector for precise threat mitigation and priority ranking.

6. ThreatZoom: Employs an adaptive hierarchical neural network to classify CVEs into their corresponding CWE classes by analyzing both statistical and semantic features from CVE descriptions.

7. V2W-BERT: A framework that utilizes transformer-based learning to map CVEs to CWEs effectively, leveraging natural language processing and transfer learning for high prediction accuracy.

8. SeqTrans: Applies sequence-to-sequence learning models to automatically generate fixes for vulnerabilities, incorporating data flow dependencies into code sequences for improved accuracy in statement-level and CVE-level fixes.

9. CWE with Environmental CVSS Calculator: Computes an average CVSS score for the CVEs associated with a given CWE, providing an estimate of its severity. It utilizes data from the National Vulnerability Database (NVD) and allows customization through timeboxing, CWE normalization, and the application of CVSS environmental modifiers.

10. Automated Mapping of CVE Vulnerability Records to MITRE CWE Weaknesses: Presents a methodology that leverages natural language processing and machine learning techniques to automate the mapping of CVE records to CWE categories, aiding in the standardization and analysis of vulnerabilities.

These tools and methodologies enhance the efficiency and accuracy of vulnerability management processes by automating the mapping of CVEs to CWEs, enabling organizations to proactively address software weaknesses and mitigate potential security risks.

[Crashedmind]

ScholarGPT Here are some of the latest studies on mapping CWE (Common Weakness Enumeration) weaknesses to vulnerabilities, focusing on techniques for better identification, automated mapping, and cybersecurity applications:

1. CVE2CWE: Automated Mapping of Software Vulnerabilities to Weaknesses Authors: M. Albanese, O. Adebiyi, F. Onovae Summary: This paper introduces an automated approach for mapping CVEs to corresponding CWEs using machine learning models, enhancing the process of identifying the most likely CWE category for previously unseen vulnerabilities. . The proposed method utilizes TF-IDF vector representations to model CWEs and CVEs and assess the semantic similarity between CWEs and previously unseen CVEs, facilitating accurate and efficient mapping. Link: Read PDF
2. Predicting Attack Paths Using Multi-Layer Perceptron Authors: J. Pickard, S. Kasturi, X. Li Summary: This research proposes a predictive model using neural networks to link vulnerabilities to specific CWE categories, focusing on building effective attack paths by understanding the underlying weaknesses. Link: Read Article
3. Combining Cybersecurity Intelligence for Automotive Threats Authors: F. Sommer, M. Gierl, R. Kriesten Summary: This paper discusses mapping CAPEC attack patterns to CWE weaknesses to refine automotive cybersecurity threat analysis, leveraging intelligence from various datasets. Link: Read PDF
4. Analyzing Threat Vectors in ICS Cyberattacks Authors: I. Villanueva-Miranda, M. Akbar Summary: The study examines the vulnerabilities in Industrial Control Systems (ICS), linking specific CVEs to CWEs like CWE-22 and CWE-284, providing a framework for mapping weaknesses in critical infrastructure. Link: Read PDF
5. Development of an Ontology-based Tool for Risk Assessment Automation Author: C. Casalini Summary: This research proposes an ontology-based tool that integrates CWE and CVE data, automating the risk assessment process and improving threat identification using a structured mapping approach. Link: Read Thesis
6. VTT-LLM: Advancing Vulnerability-to-Tactic-and-Technique Mapping Authors: C. Zhang, L. Wang, T. Zhou Summary: The study introduces VTT-LLM, a model for mapping vulnerabilities to ATT&CK tactics using advanced NLP techniques and a large language model fine-tuning approach, incorporating CWE weaknesses. Link: Read PDF
7. What Can Self-Admitted Technical Debt Tell Us About Security? Authors: NED Ferreyra, M. Shahin, M. Zahedi Summary: This paper explores the correlation between self-admitted technical debt (SATD) and security vulnerabilities, mapping instances to specific CWE categories for a deeper analysis of software security issues. Link: Read PDF
8. Large Language Model as Synthesizer for Vulnerability Repair Authors: X. Zhou, K. Kim, B. Xu, D. Lo Summary: The research leverages a large language model for synthesizing input data to map code vulnerabilities to CWE categories, improving automated vulnerability repair strategies. Link: Read PDF These papers reflect the most recent advancements in automated mapping techniques, integration with AI and ML models, and enhanced frameworks for better risk assessment and threat prediction.

[Crashedmind]

https://scholar.google.com/scholar?hl=en&as_sdt=0%2C5&q=cwe+mapping&btnG=

Text2Weak: mapping CVEs to CWEs using description embeddings analysis https://ris.utwente.nl/ws/portalfiles/portal/453564452/Submission_8_final.pdf

Automated Mapping of CVE Vulnerability Records to MITRE CWE Weaknesses Ashraf Haddad, Najwa Aaraj, Preslav Nakov, Septimiu Fabian Mare In recent years, a proliferation of cyber-security threats and diversity has been on the rise culminating in an increase in their reporting and analysis. To counter that, many non-profit organizations have emerged in this domain, such as MITRE and OSWAP, which have been actively tracking vulnerabilities, and publishing defense recommendations in standardized formats. As producing data in such formats manually is very time-consuming, there have been some proposals to automate the process. Unfortunately, a major obstacle to adopting supervised machine learning for this problem has been the lack of publicly available specialized datasets. Here, we aim to bridge this gap. In particular, we focus on mapping CVE records into MITRE CWE Weaknesses, and we release to the research community a manually annotated dataset of 4,012 records for this task. With a human-in-the-loop framework in mind, we approach the problem as a ranking task and aim to incorporate reinforced learning to make use of the human feedback in future work. Our experimental results using fine-tuned deep learning models, namely Sentence-BERT and rankT5, show sizable performance gains over BM25, BERT, and RoBERTa, which demonstrates the need for an architecture capable of good semantic understanding for this task.

CVE2CWE: Automated Mapping of Software Vulnerabilities to Weaknesses Based on CVE Descriptions Massimiliano Albanese a , Olutola Adebiyi b and Frank Onovae c Center for Secure Information Systems, George Mason University, Fairfax, U.S.A. Keywords: Vulnerabilities, Vulnerability Classification, Security Metrics, Software Weaknesses. Abstract: Vulnerabilities in software systems are inevitable, but proper mitigation strategies can greatly reduce the risk to organizations. The Common Vulnerabilities and Exposures (CVE) list makes vulnerability information readily available and organizations rely on this information to effectively mitigate vulnerabilities in their systems. CVEs are classified into Common Weakness Enumeration (CWE) categories based on their underlying weaknesses and semantics. This classification provides an understanding of software flaws, their potential impacts, and means to detect, fix and prevent them. This understanding can help security administrators efficiently allocate resources to address critical security issues. However, mapping of CVEs to CWEs is mostly a manual process. To address this limitation, we introduce CVE2CWE, an automated approach for mapping Common Vulnerabilities and Exposures (CVEs) to Common Weakness Enumeration (CWE) entries. Leveraging natural language processing techniques, CVE2CWE extracts relevant information from CVE descriptions and maps them to corresponding CWEs. The proposed method utilizes TF-IDF vector representations to model CWEs and CVEs and assess the semantic similarity between CWEs and previously unseen CVEs, facilitating accurate and efficient mapping. Experimental results demonstrate the effectiveness of CVE2CWE in automating the vulnerability-to-weakness mapping process, thereby aiding cybersecurity professionals in prioritizing and addressing software vulnerabilities more effectively. Additionally, we study the similarities and overlaps between CWEs and quantitatively assess their impact on the classification process.

Automated CVE Analysis for Threat Prioritization and Impact Prediction

Ehsan Aghaei, Ehab Al-Shaer, Waseem Shadid, Xi Niu

The Common Vulnerabilities and Exposures (CVE) are pivotal information for proactive cybersecurity measures, including service patching, security hardening, and more. However, CVEs typically offer low-level, product-oriented descriptions of publicly disclosed cybersecurity vulnerabilities, often lacking the essential attack semantic information required for comprehensive weakness characterization and threat impact estimation. This critical insight is essential for CVE prioritization and the identification of potential countermeasures, particularly when dealing with a large number of CVEs. Current industry practices involve manual evaluation of CVEs to assess their attack severities using the Common Vulnerability Scoring System (CVSS) and mapping them to Common Weakness Enumeration (CWE) for potential mitigation identification. Unfortunately, this manual analysis presents a major bottleneck in the vulnerability analysis process, leading to slowdowns in proactive cybersecurity efforts and the potential for inaccuracies due to human errors. In this research, we introduce our novel predictive model and tool (called CVEDrill) which revolutionizes CVE analysis and threat prioritization. CVEDrill accurately estimates the CVSS vector for precise threat mitigation and priority ranking and seamlessly automates the classification of CVEs into the appropriate CWE hierarchy classes. By harnessing CVEDrill, organizations can now implement cybersecurity countermeasure mitigation with unparalleled accuracy and timeliness, surpassing in this domain the capabilities of state-of-the-art tools like ChaptGPT.

Subjects: | Cryptography and Security (cs.CR); Machine Learning (cs.LG) -- | -- Cite as: | arXiv:2309.03040 [cs.CR]

Automated CVE Analysis for Threat Prioritization and Impact Prediction [Ehsan Aghaei](https://arxiv.org/search/cs?searchtype=author&query=Aghaei,+E), [Ehab Al-Shaer](https://arxiv.org/search/cs?searchtype=author&query=Al-Shaer,+E), [Waseem Shadid](https://arxiv.org/search/cs?searchtype=author&query=Shadid,+W), [Xi Niu](https://arxiv.org/search/cs?searchtype=author&query=Niu,+X) The Common Vulnerabilities and Exposures (CVE) are pivotal information for proactive cybersecurity measures, including service patching, security hardening, and more. However, CVEs typically offer low-level, product-oriented descriptions of publicly disclosed cybersecurity vulnerabilities, often lacking the essential attack semantic information required for comprehensive weakness characterization and threat impact estimation. This critical insight is essential for CVE prioritization and the identification of potential countermeasures, particularly when dealing with a large number of CVEs. Current industry practices involve manual evaluation of CVEs to assess their attack severities using the Common Vulnerability Scoring System (CVSS) and mapping them to Common Weakness Enumeration (CWE) for potential mitigation identification. Unfortunately, this manual analysis presents a major bottleneck in the vulnerability analysis process, leading to slowdowns in proactive cybersecurity efforts and the potential for inaccuracies due to human errors. In this research, we introduce our novel predictive model and tool (called CVEDrill) which revolutionizes CVE analysis and threat prioritization. CVEDrill accurately estimates the CVSS vector for precise threat mitigation and priority ranking and seamlessly automates the classification of CVEs into the appropriate CWE hierarchy classes. By harnessing CVEDrill, organizations can now implement cybersecurity countermeasure mitigation with unparalleled accuracy and timeliness, surpassing in this domain the capabilities of state-of-the-art tools like ChaptGPT. Subjects: Cryptography and Security (cs.CR); Machine Learning (cs.LG) Cite as: [arXiv:2309.03040](https://arxiv.org/abs/2309.03040) [cs.CR] https://ris.utwente.nl/ws/portalfiles/portal/453563915/NLPAICS_submission.pdf Comprehensive threat analysis and systematic mapping of CVEs to MITRE framework Stefano Simonetto University of Twente s.simonetto@utwente.nl Peter Bosch University of Twente h.g.p.bosch@utwente.nl VWC-BERT: Scaling Vulnerability–Weakness–Exploit Mapping on Modern AI Accelerators https://ieeexplore.ieee.org/abstract/document/10020622 Defending cybersystems needs accurate mapping of software and hardware vulnerabilities to generalized descriptions of weaknesses, and weaknesses to exploits. These mappings enable cyber defenders to build plans for effective defense and assessment of potential risks to a cybersystem. With close to 200k vulnerabilities, manual mapping is not a feasible option. However, automated mapping is challenging due to limited training data, computational intractability, and limitations in computational natural language processing. Tools based on breakthroughs in Transformer-based language models have been demonstrated to classify vulnerabilities with high accuracy. We make three key contributions in this paper: (1) We present a new framework, VWC-BERT, that augments the Transformer-based hierarchical multi-class classification framework of Das et al. (V2W-BERT) with the ability to map weaknesses to exploits. (2) We implement VWC-BERT on modern AI accelerator platforms using two data parallel techniques for the pre-training phase and demonstrate nearly linear speedups across NVIDIA accelerator platforms. We observe nearly linear speedups for up to 16 V100 and 8 A100 GPUs, and about 3.4× speedup for A100 relative to V100 GPUs. Enabled by scaling, we also demonstrate higher accuracy using a larger language model, RoBERTa-Large. We show up to 87% accuracy for strict and up to 98% accuracy for relaxed classification. (3) We develop a novel parallel link manager for the link prediction phase and demonstrate up to 21× speedup with 16 V100 GPUs relative to one V100 GPU, and thus reducing the runtime from 2.5 hours to 10 minutes. We believe that generalizability and scalability of VWC-BERT will benefit both the theoretical development and practical deployment of novel cyberdefense solutions and vulnerability classification. V2W-BERT: A Framework for Effective Hierarchical Multiclass Classification of Software Vulnerabilities Abstract: We consider the problem of automating the mapping of observed vulnerabilities in software listed in Common Vulnerabilities and Exposures (CVE) reports to weaknesses listed in Common Weakness Enumerations (CWE) reports, a hierarchically designed dictionary of software weaknesses. Mapping of CVEs to CWEs provides a means to understand how they might be exploited for malicious purposes, and to mitigate their impact. Since manual mapping of CVEs to CWEs is not a viable approach due to their ever-increasing sizes, automated approaches need to be devised but obtaining highly accurate mapping is a challenging problem. We present a novel Transformer-based learning framework (V2W-BERT) in this paper to solve this problem by bringing together 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 for rare CWE classes with little or no data. Using vulnerability and weakness reports from MITRE and the 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 demonstrate significant improvements in using historical data to predict weaknesses for future instances of CVEs. We believe that our work will would influence the design of better automated mapping approaches, and also that this technology could be deployed for more effective cybersecurity. Published in: [2021 IEEE 8th International Conference on Data Science and Advanced Analytics (DSAA)](https://ieeexplore.ieee.org/xpl/conhome/9564091/proceeding) VulnBERTa: On Automating CWE Weakness Assignment and Improving the Quality of Cybersecurity CVE Vulnerabilities Through ML/NLP Abstract: Vulnerability management is a critical industry activity driven by compliance and regulations aiming to efficiently allocate best-fitted resources to address vulnerabilities. The cybersecurity community is global; thus, the produced vulnerability reports vary in quality and perspective. To tackle the discrepancies, machine learning (ML) has shown promise in automating vulnerability assignments. While some existing ML approaches have demonstrated feasibility, there is room for improvement. Additionally, gaps remain in the literature in understanding how the specific terminology used in vulnerability databases and reports influences ML interpretation. In this paper, we aim to close several such gaps. First, based on the RoBERTa transformer architecture, we introduce a systematic methodology to assign Common Weakness Enumerations (CWE)-related information to a vulnerability description automatically. For that purpose, we develop a cybersecurity-focused model, VulnBERTa. Second, we apply our VulnBERTa model(s) to retroactively and automatically assign CWEs to unassigned National Institute of Standards and Technology (NIST) National Vulnerability Database (NVD) entries, thus improving the quality of open cybersecurity data. Our pre-liminary results are on par with comparable state-of-the-art while achieving higher classification granularity and scale. Published in: [2024 IEEE European Symposium on Security and Privacy Workshops (EuroS&PW)](https://ieeexplore.ieee.org/xpl/conhome/10628447/proceeding) [Not The End of Story: An Evaluation of ChatGPT-Driven Vulnerability Description Mappings](https://aclanthology.org/2023.findings-acl.229.pdf) [Xin Liu](https://aclanthology.org/people/x/xin-liu/), [Yuan Tan](https://aclanthology.org/people/y/yuan-tan/), [Zhenghang Xiao](https://aclanthology.org/people/z/zhenghang-xiao/), [Jianwei Zhuge](https://aclanthology.org/people/j/jianwei-zhuge/), [Rui Zhou](https://aclanthology.org/people/r/rui-zhou/) Abstract As the number of vulnerabilities increases day by day, security management requires more and more structured data. In addition to textual descriptions of vulnerabilities, security engineers must classify and assess vulnerabilities and clarify their associated techniques. Vulnerability Description Mapping (VDM) refers to mapping vulnerabilities to Common Weakness Enumeration (CWE), Common Attack Pattern Enumeration and Classification, ATT&CK Techniques, and other classifications. Accurate VDM is necessary to reduce the pressure of security management and improve the speed of security emergency response. ChatGPT is the latest state-of-the-art closed-source conversational large language model (LLM), which performs excellently on many tasks. This paper explores the application of closed-source LLMs to real-world security management scenarios by evaluating ChatGPT’s performance on VDM tasks. The results show that although ChatGPT may be close to the level of human experts on some tasks, it still cannot replace the critical role of professional security engineers in vulnerability analysis. In a word, closed-source LLM is not the end of story. https://aclanthology.org/2023.findings-acl.229.pdf