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GIAC Machine Learning Engineer

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Q_01 domain/d1 · medium single discuss →

A security analyst needs to build a system that flags anomalies in log data without relying on any predefined signatures or human tuning of detection rules. Which deep learning architecture, as taught in the course, is specifically applied to this signature-free log anomaly detection task?

A Convolutional neural networks trained on labeled malware samples
B Autoencoders using reconstruction loss to identify anomalies
C Support Vector Machines with kernel functions
D Random Forests built from boosted decision trees

Q_02 domain/d1 · medium single discuss →

During Section 4, students build a real-time network protocol classification system and then add anomaly detection to it. Why is this described as unusual relative to typical practice?

A Anomaly detection is a task typically reserved for unsupervised approaches, yet here it is implemented within a supervised classification system
B Protocol classification cannot normally be performed in real time
C Autoencoders are usually required for any form of protocol identification
D Anomaly detection normally requires labeled malware data that is unavailable for protocols

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