Automated CVE Analysis for Threat Prioritization and Impact Prediction
- URL: http://arxiv.org/abs/2309.03040v1
- Date: Wed, 6 Sep 2023 14:34:03 GMT
- Title: Automated CVE Analysis for Threat Prioritization and Impact Prediction
- Authors: Ehsan Aghaei, Ehab Al-Shaer, Waseem Shadid, Xi Niu
- Abstract summary: 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.
- Score: 4.540236408836132
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: 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.
Related papers
- Efficacy of EPSS in High Severity CVEs found in KEV [0.0]
The Exploit Prediction Scoring System (EPSS) is designed to assess the probability of a vulnerability being exploited in the next 30 days.
This study evaluates EPSS's ability to predict exploitation before vulnerabilities are actively compromised.
arXiv Detail & Related papers (2024-11-04T21:12:58Z) - The Impact of SBOM Generators on Vulnerability Assessment in Python: A Comparison and a Novel Approach [56.4040698609393]
Software Bill of Materials (SBOM) has been promoted as a tool to increase transparency and verifiability in software composition.
Current SBOM generation tools often suffer from inaccuracies in identifying components and dependencies.
We propose PIP-sbom, a novel pip-inspired solution that addresses their shortcomings.
arXiv Detail & Related papers (2024-09-10T10:12:37Z) - SecScore: Enhancing the CVSS Threat Metric Group with Empirical Evidences [0.0]
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.
arXiv Detail & Related papers (2024-05-14T12:25:55Z) - A Zero Trust Framework for Realization and Defense Against Generative AI
Attacks in Power Grid [62.91192307098067]
This paper proposes a novel zero trust framework for a power grid supply chain (PGSC)
It facilitates early detection of potential GenAI-driven attack vectors, assessment of tail risk-based stability measures, and mitigation of such threats.
Experimental results show that the proposed zero trust framework achieves an accuracy of 95.7% on attack vector generation, a risk measure of 9.61% for a 95% stable PGSC, and a 99% confidence in defense against GenAI-driven attack.
arXiv Detail & Related papers (2024-03-11T02:47:21Z) - Unveiling Hidden Links Between Unseen Security Entities [3.7138962865789353]
VulnScopper is an innovative approach that utilizes multi-modal representation learning, combining Knowledge Graphs (KG) and Natural Processing (NLP)
We evaluate VulnScopper on two major security datasets, the National Vulnerability Database (NVD) and the Red Hat CVE database.
Our results show that VulnScopper outperforms existing methods, achieving up to 78% Hits@10 accuracy in linking CVEs to Common Vulnerabilities and Exposures (CWEs), and Common Platform Languageions (CPEs)
arXiv Detail & Related papers (2024-03-04T13:14:39Z) - CVE-driven Attack Technique Prediction with Semantic Information
Extraction and a Domain-specific Language Model [2.1756081703276]
The paper introduces the TTPpredictor tool, which uses innovative techniques to analyze CVE descriptions and infer plausible TTP attacks resulting from CVE exploitation.
TTPpredictor overcomes challenges posed by limited labeled data and semantic disparities between CVE and TTP descriptions.
The paper presents an empirical assessment, demonstrating TTPpredictor's effectiveness with accuracy rates of approximately 98% and F1-scores ranging from 95% to 98% in precise CVE classification to ATT&CK techniques.
arXiv Detail & Related papers (2023-09-06T06:53:45Z) - On the Security Risks of Knowledge Graph Reasoning [71.64027889145261]
We systematize the security threats to KGR according to the adversary's objectives, knowledge, and attack vectors.
We present ROAR, a new class of attacks that instantiate a variety of such threats.
We explore potential countermeasures against ROAR, including filtering of potentially poisoning knowledge and training with adversarially augmented queries.
arXiv Detail & Related papers (2023-05-03T18:47:42Z) - Adversarial defense for automatic speaker verification by cascaded
self-supervised learning models [101.42920161993455]
More and more malicious attackers attempt to launch adversarial attacks at automatic speaker verification (ASV) systems.
We propose a standard and attack-agnostic method based on cascaded self-supervised learning models to purify the adversarial perturbations.
Experimental results demonstrate that the proposed method achieves effective defense performance and can successfully counter adversarial attacks.
arXiv Detail & Related papers (2021-02-14T01:56:43Z) - A System for Automated Open-Source Threat Intelligence Gathering and
Management [53.65687495231605]
SecurityKG is a system for automated OSCTI gathering and management.
It uses a combination of AI and NLP techniques to extract high-fidelity knowledge about threat behaviors.
arXiv Detail & Related papers (2021-01-19T18:31:35Z) - ThreatZoom: CVE2CWE using Hierarchical Neural Network [4.254099382808598]
One or more CVEs are grouped into the Common Weakness Exposureion (CWE) classes.
Thousands of critical and new CVEs remain unclassified, yet they are unpatchable.
This paper presents the first automatic tool to classify CVEs to CWEs.
arXiv Detail & Related papers (2020-09-24T06:04:56Z) - Investigating Robustness of Adversarial Samples Detection for Automatic
Speaker Verification [78.51092318750102]
This work proposes to defend ASV systems against adversarial attacks with a separate detection network.
A VGG-like binary classification detector is introduced and demonstrated to be effective on detecting adversarial samples.
arXiv Detail & Related papers (2020-06-11T04:31:56Z)
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.