SecScore: Enhancing the CVSS Threat Metric Group with Empirical Evidences
- URL: http://arxiv.org/abs/2405.08539v1
- Date: Tue, 14 May 2024 12:25:55 GMT
- Title: SecScore: Enhancing the CVSS Threat Metric Group with Empirical Evidences
- Authors: Miguel Santana, Vinicius V. Cogo, Alan Oliveira de Sá,
- Abstract summary: One of the most widely used vulnerability scoring systems (CVSS) does not address the increasing likelihood of emerging an exploit code.
We present SecScore, an innovative vulnerability severity score that enhances CVSS Threat metric group.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Background: Timely prioritising and remediating vulnerabilities are paramount in the dynamic cybersecurity field, and one of the most widely used vulnerability scoring systems (CVSS) does not address the increasing likelihood of emerging an exploit code. Aims: We present SecScore, an innovative vulnerability severity score that enhances CVSS Threat metric group with statistical models from empirical evidences of real-world exploit codes. Method: SecScore adjusts the traditional CVSS score using an explainable and empirical method that more accurately and promptly captures the dynamics of exploit code development. Results: Our approach can integrate seamlessly into the assessment/prioritisation stage of several vulnerability management processes, improving the effectiveness of prioritisation and ensuring timely remediation. We provide real-world statistical analysis and models for a wide range of vulnerability types and platforms, demonstrating that SecScore is flexible according to the vulnerability's profile. Comprehensive experiments validate the value and timeliness of SecScore in vulnerability prioritisation. Conclusions: SecScore advances the vulnerability metrics theory and enhances organisational cybersecurity with practical insights.
Related papers
- 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) - SecCodePLT: A Unified Platform for Evaluating the Security of Code GenAI [47.11178028457252]
We develop SecCodePLT, a unified and comprehensive evaluation platform for code GenAIs' risks.
For insecure code, we introduce a new methodology for data creation that combines experts with automatic generation.
For cyberattack helpfulness, we construct samples to prompt a model to generate actual attacks, along with dynamic metrics in our environment.
arXiv Detail & Related papers (2024-10-14T21:17:22Z) - 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) - Boosting Cybersecurity Vulnerability Scanning based on LLM-supported Static Application Security Testing [5.644999288757871]
Large Language Models (LLMs) have demonstrated powerful code analysis capabilities, but their static training data and privacy risks limit their effectiveness.
We propose LSAST, a novel approach that integrates LLMs with SAST scanners to enhance vulnerability detection.
We set a new benchmark for static vulnerability analysis, offering a robust, privacy-conscious solution.
arXiv Detail & Related papers (2024-09-24T04:42:43Z) - A Relevance Model for Threat-Centric Ranking of Cybersecurity Vulnerabilities [0.29998889086656577]
The relentless process of tracking and remediating vulnerabilities is a top concern for cybersecurity professionals.
We provide a framework for vulnerability management specifically focused on mitigating threats using adversary criteria derived from MITRE ATT&CK.
Our results show an average 71.5% - 91.3% improvement towards the identification of vulnerabilities likely to be targeted and exploited by cyber threat actors.
arXiv Detail & Related papers (2024-06-09T23:29:12Z) - RigorLLM: Resilient Guardrails for Large Language Models against Undesired Content [62.685566387625975]
Current mitigation strategies, while effective, are not resilient under adversarial attacks.
This paper introduces Resilient Guardrails for Large Language Models (RigorLLM), a novel framework designed to efficiently moderate harmful and unsafe inputs.
arXiv Detail & Related papers (2024-03-19T07:25:02Z) - Automated CVE Analysis for Threat Prioritization and Impact Prediction [4.540236408836132]
We introduce our novel predictive model and tool (called CVEDrill) which revolutionizes CVE analysis and threat prioritization.
CVEDrill accurately estimates the Common Vulnerability Scoring System (CVSS) vector for precise threat mitigation and priority ranking.
It seamlessly automates the classification of CVEs into the appropriate Common Weaknession (CWE) hierarchy classes.
arXiv Detail & Related papers (2023-09-06T14:34:03Z) - 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) - Anomaly Detection Based on Selection and Weighting in Latent Space [73.01328671569759]
We propose a novel selection-and-weighting-based anomaly detection framework called SWAD.
Experiments on both benchmark and real-world datasets have shown the effectiveness and superiority of SWAD.
arXiv Detail & Related papers (2021-03-08T10:56:38Z) - SAMBA: Safe Model-Based & Active Reinforcement Learning [59.01424351231993]
SAMBA is a framework for safe reinforcement learning that combines aspects from probabilistic modelling, information theory, and statistics.
We evaluate our algorithm on a variety of safe dynamical system benchmarks involving both low and high-dimensional state representations.
We provide intuition as to the effectiveness of the framework by a detailed analysis of our active metrics and safety constraints.
arXiv Detail & Related papers (2020-06-12T10:40:46Z)
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.