AWS Certified Cloud Practitioner
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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
A
Unsupervised Learning
B
Supervised Learning
C
Reinforcement Learning
D
Regression
Explanation:
Explanation
Unsupervised Learning is the correct approach because:
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No predefined labels: The team doesn't have attack labels (i.e., they don't know what constitutes 'normal' vs 'attack' traffic beforehand).
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Pattern discovery: Unsupervised learning algorithms like clustering, anomaly detection, or association can identify unusual patterns without prior labeling.
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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:
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B) Supervised Learning: Requires labeled data (attack vs normal traffic) which the team doesn't have.
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C) Reinforcement Learning: Involves learning through trial-and-error with rewards/penalties, not suitable for pattern detection without interaction.
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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.
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