**AI-Powered Vulnerability Scanning: Uncovering Hidden Threats**
As security threats continue to evolve, it’s essential to stay ahead of the curve by leveraging the power of AI-assisted vulnerability scanning. In this article, we’ll dive into the world of machine learning and semantic embeddings, showing how they can reorder CVE vulnerabilities beyond raw CVSS scores. Get ready to uncover the most critical threats and gain valuable insights into the vulnerability landscape.
**Getting Started with the Dataset**
To begin, we fetched recent CVEs using the `CVEDataFetcher` class, limiting the dataset to 50 vulnerabilities and dates within the past 30 days. Here’s a snapshot of our dataset:
Dataset Overview:
• Whole CVEs: 50
• Date Range: March 10, 2022, to April 10, 2022
• Severity Breakdown: LOW: 20, MEDIUM: 15, HIGH: 8, CRITICAL: 7
**Uncovering the Hidden Story**
Next, we extracted semantic features from the vulnerability descriptions using the `VulnerabilityFeatureExtractor` class. This step enabled us to capture the meaning and context behind each vulnerability, which we used to train machine learning models. These models predicted the severity and priority of each vulnerability, giving us a more comprehensive understanding of the threats.
**Prioritizing the Threats**
We assigned the predicted priority scores, severity probabilities, and score predictions to the vulnerabilities, ranking them by priority. But we didn’t stop there. We also used the `VulnerabilityAnalyzer` class to cluster the vulnerabilities, identifying patterns and correlations among the threats.
**Visualizing the Results**
The `visualize_results` function brought all the findings together, presenting a comprehensive dashboard with various plots and charts. These visualizations provided a clear understanding of the vulnerability distribution, priority ratings, and severity levels.
**The Top 10 Priority Vulnerabilities**
After analyzing the data, we identified the top 10 priority vulnerabilities, along with their CVSS scores, severity levels, and descriptions. This information enables security professionals to focus on the most critical threats and take targeted action.
**The Bottom Line**
In this article, we’ve demonstrated the power of AI-assisted vulnerability scanning, showing how machine learning and semantic embeddings can help identify the most critical threats. By analyzing the results and visualizing the findings, security professionals can gain valuable insights into the vulnerability landscape, enabling more effective risk management and mitigations.
**Stay Tuned for More**
That’s all for now, but we’re just getting started. Stay tuned for more articles on AI-assisted vulnerability scanning and related topics. We’ll be exploring new techniques and technologies, and sharing our findings with the security community.
