Summary

In this chapter, we explored key statistical concepts—correlation, causation, bias, and variance—and their critical roles in AI-driven cybersecurity. Understanding these concepts is crucial for designing, implementing, and evaluating effective security systems.

We started by distinguishing between correlation and causation, emphasizing the importance of accurate data interpretation. Through examples and case studies, we showed how misconceptions could lead to ineffective cybersecurity strategies. We also discussed managing bias and variance in AI models, introducing techniques such as regularization and cross-validation to enhance model robustness and performance in real-world scenarios. By employing the tools and methods outlined, cybersecurity professionals can not only improve the reliability of AI systems but also ensure that they are responsible and transparent.

This chapter underscores the necessity of continuous learning in the ever-evolving fields...