Introducing bias and variance

In the context of building and deploying AI models in cybersecurity, two frequently encountered terms are bias and variance. These statistical concepts are critical to your understanding because they directly affect the performance and reliability of your models. Let’s explore what each of these terms means and how they influence your AI systems.

Bias

Bias in AI refers to the error introduced into your model due to oversimplification of the machine learning algorithm. It occurs when the model is too simple to capture the complex patterns in the data, or when it’s working under wrongful assumptions about the data. In cybersecurity, a biased model might consistently fail to detect a new type of malware because it hasn’t been exposed to sufficient or diverse examples during training.

Imagine you’re using a model trained primarily on data from network intrusions that occurred during business hours. If this model is deployed...