Which of the following BEST describes how supervised learning models help reduce false positives in cybersecurity threat detection?

Supervised learning is a machine learning paradigm where an algorithm learns from a dataset that has been manually labeled with the correct outcomes. In cybersecurity, this involves training a model on historical data where events are explicitly tagged as either "malicious" or "benign." The model learns the patterns and features that distinguish these two classes. By training on a high-quality, well-labeled dataset that accurately represents both legitimate and threatening activities, the model can build a robust decision boundary. This allows it to more accurately classify new, unseen data, thereby reducing the number of times it incorrectly flags legitimate activity as a threat (a false positive).

A. This describes unsupervised learning (e.g., clustering), which finds inherent patterns to group data without relying on pre-existing labels.

B. While some advanced models can adjust in real-time (online learning), the fundamental principle of supervised learning is training on a static, historical labeled dataset.

D. This describes techniques like data augmentation or synthetic data generation, which are used to supplement a training set, not the core learning mechanism itself.

1. ISACA. (2021). Artificial Intelligence for Auditing. "Supervised learning uses labeled data sets to train algorithms to classify data or predict outcomes accurately. With supervised learning

the enterprise provides the AI model with both inputs and desired outputs." (Page 8

"Supervised Learning" section).

2. Xin

Y.

Kong

L.

Liu

Z.

Chen

Y.

Li

Y.

Zhu

H.

... & Wang

C. (2018). Machine learning and deep learning methods for cybersecurity. IEEE Access

6

35365-35381. "In supervised learning

the training data consist of a set of training examples

where each example is a pair consisting of an input object (typically a vector) and a desired output value (also called the supervisory signal)." (Section II-A

Paragraph 1). https://doi.org/10.1109/ACCESS.2018.2837699

3. Ng

A. (2008). CS229 Machine Learning Course Notes. Stanford University. "In supervised learning

we are given a data set and already know what our correct output should look like

having the idea that there is a relationship between the input and the output." (Part I

"Supervised Learning

" Page 2).