AWS Certified Cloud Practitioner
Get started today
Ultimate access to all questions.
Deep dive into the quiz with AI chat providers.
We prepare a focused prompt with your quiz and certificate details so each AI can offer a more tailored, in-depth explanation.
A cybersecurity team wants to detect unusual traffic patterns that may indicate a network attack, but they don't have predefined attack labels. Which approach is suitable?
Real Exam
Community
RRitesh
0
A
Unsupervised Learning
B
Supervised Learning
C
Reinforcement Learning
D
Regression
Explanation:
Explanation
Unsupervised Learning is the correct approach because:
- No predefined labels: The team doesn't have attack labels (i.e., they don't know what constitutes 'normal' vs 'attack' traffic beforehand).
- Pattern discovery: Unsupervised learning algorithms like clustering, anomaly detection, or association can identify unusual patterns without prior labeling.
- Anomaly detection: This is a classic use case for unsupervised learning where the algorithm learns what 'normal' traffic looks like and flags deviations.
Why other options are incorrect:
- B) Supervised Learning: Requires labeled data (attack vs normal traffic) which the team doesn't have.
- C) Reinforcement Learning: Involves learning through trial-and-error with rewards/penalties, not suitable for pattern detection without interaction.
- D) Regression: A type of supervised learning for predicting continuous values, requires labeled training data.
Real-world application: AWS services like Amazon GuardDuty use machine learning (including unsupervised techniques) to detect unusual API calls, potentially unauthorized deployments, and other suspicious activities without requiring predefined attack signatures.
Comments
Loading comments...