11 Advanced statistical analysis

 

This chapter covers

  • Statistical techniques in practical terms
  • Analyzing patterns in cybersecurity data
  • Real-world examples to apply statistical tools
  • Evaluating security strategies with actionable insights

This is not a mathematics book. Most cybersecurity practitioners will not be crunching numbers by hand or writing scripts to perform statistical data analysis. However, it is essential to understand the basic principles that underpin the metrics used to guide decision-making. These statistical calculations are often handled by a dedicated analyst or software solution, executing algorithms to generate actionable insights. Such in-depth understanding allows cybersecurity professionals at all levels to better interpret the data, identify trends, and make informed decisions about their security posture.

Statistics in cybersecurity help quantify risk, predict potential threats, and assess the effectiveness of security controls. For example, statistical trend analysis allows organizations to detect patterns in attack vectors, while correlation techniques can reveal relationships between seemingly distinct security incidents. These insights are invaluable for fine-tuning security strategies and making data-driven decisions.

11.1 Continuous improvement with statistical metrics

11.1.1 Key concepts for continuous improvement

11.1.2 Free resources for statistical analysis

11.2 Using statistical metrics for continuous improvement

11.3 Implementing a feedback loop

11.4 Finding hidden relationships in data

11.5 Using moving averages to track trends

11.5.1 Moving averages example

11.5.2 Detailed description of the plots

11.6 Grouping similar events for better insights

11.7 Forecasting cybersecurity trends

11.7.1 ARIMA in action

11.8 Bayesian inference

11.8.1 Interpreting the results

11.9 Statistical models for vulnerability management

11.9.1 Interpreting the results

Summary