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z scores are the standardized values obtained after converting a normal distribution into a standard normal distribution. A z score is measured in units of the standard deviation. The z score tells you how many standard deviations the value x is above (to the right of) or below (to the left of) the mean, μ. Values of x that are larger than the mean have positive z scores, and values of x that are smaller than the mean have negative z scores. If x equals the mean, then x has a z score of...
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A z score (or standardized value) is measured in units of the standard deviation. It indicates how many standard deviations the value x is above (to the right of) or below (to the left of) the mean, μ. Values of x that are larger than the mean have positive z scores, and values of x that are smaller than the mean have negative z scores. If x equals the mean, then x has a zero z score. It is important to note that the mean of the z scores is zero, and the standard deviation is one.
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The confidence coefficient is also known as the confidence level or degree of confidence. It is the percent expression for the probability, 1-α, that the confidence interval contains the true population parameter assuming that the confidence interval is obtained after sufficient unbiased sampling; for example, if the CL = 90%, then in 90 out of 100 samples the interval estimate will enclose the true population parameter. Here α is the area under the curve, distributed equally under...
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The critical region, critical value, and significance level are interdependent concepts crucial in hypothesis testing.
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Sometimes, there is a need to convert from one unit to another one. For instance, reading a cookbook in which quantities are expressed in units of liters or ounces may require conversion of quantities to cups. Or, when looking up directions on how to get to a location, we may be interested to know how many miles we are going to walk. In this case, we would have to convert units of feet or meters to miles.
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Standard deviation measures the spread of data around the mean value. Many large data sets follow a Gaussian distribution, also known as a normal distribution. This distribution is bell-shaped curved, with the most frequently observed value (mean or central value) in the middle. The farther away from the central value, the greater the deviation from the central value, and the lower the frequency.
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Support for the Vulnerability Management Process Using Conversion CVSS Base Score 2.0 to 3.x.

Maciej Roman Nowak1, Michał Walkowski1, Sławomir Sujecki1

  • 1Department of Telecommunications and Teleinformatics, Wroclaw University of Science and Technology, 50-370 Wroclaw, Poland.

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Summary

This study introduces a machine learning method to convert older Common Vulnerability Scoring System (CVSS) version 2.0 vulnerability scores to the newer CVSS 3.x standard, enhancing cybersecurity vulnerability assessment.

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CVSS standardmachine learningsecurity of IT systems

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Area of Science:

  • Cybersecurity
  • Machine Learning
  • Vulnerability Management

Background:

  • The COVID-19 pandemic increased cyber threats and the need for effective vulnerability management.
  • The Common Vulnerability Scoring System (CVSS) is crucial for prioritizing cyber risks.
  • Discrepancies exist between CVSS 2.0 and 3.x standards, hindering accurate assessments.

Purpose of the Study:

  • To develop a machine learning-based approach for converting CVSS 2.0 vulnerability vectors to the CVSS 3.x standard.
  • To address the challenge of missing or outdated CVSS scores in vulnerability databases.
  • To improve the accuracy and timeliness of critical vulnerability prioritization.

Main Methods:

  • Utilized Natural Language Processing (NLP) for data acquisition from vulnerability databases.
  • Employed machine learning algorithms for vector mapping and parameter optimization.
  • Developed a procedure for calculating CVSS 3.x vector components from CVSS 2.0 data.

Main Results:

  • Demonstrated the effectiveness of the proposed machine learning method for CVSS 2.0 to 3.x conversion.
  • Successfully calculated CVSS 3.x vector components using the developed algorithms.
  • Validated the method's capability in handling discrepancies between CVSS versions.

Conclusions:

  • The proposed machine learning approach offers an effective solution for standardizing vulnerability scoring.
  • This method enhances the ability to accurately assess and prioritize cyber vulnerabilities in dynamic threat landscapes.
  • Facilitates better cybersecurity decision-making by providing up-to-date and consistent vulnerability data.