CompTIA CySA+ Practice Test

Which system utilizes examples to classify future network traffic as malicious or benign?

1. Artificial intelligence

2. Machine learning

3. Deep learning

4. Supervised learning

The correct answer is: Machine learning

The correct choice is Machine learning because it is a subset of artificial intelligence that specifically involves the development of algorithms that allow computers to learn from and make predictions based on data. In the context of classifying network traffic, machine learning models can be trained on historical data containing labeled examples of both malicious and benign traffic. Through this training process, the model learns to recognize the patterns and characteristics associated with each category. Once trained, the model can then classify new, unseen network traffic by drawing upon what it learned during the training phase. This ability to generalize from past examples and make predictions about future data is a cornerstone of how machine learning operates, making it particularly effective for dynamic environments like network security, where new threats emerge constantly. Though elements like deep learning and supervised learning are also relevant, they pertain to narrower aspects of this broader field. Deep learning, for example, is a more complex type of machine learning that uses neural networks with many layers, focusing on unstructured data and more advanced pattern recognition. Supervised learning, while a category under machine learning that involves training with labeled data, is not as encompassing as the general term of machine learning itself. Therefore, machine learning is the most accurate and comprehensive answer regarding the classification of network traffic based on