Hands-on Lab

Introduction

In this lab, students will classify malware behaviors using system logs and a neural network.

Objectives

- Understand features that differentiate malware from benign behavior.

- Train a neural network for malware classification.

- Evaluate the model's ability to classify unseen malware data.

Lab Steps

1. Dataset Preparation:

   - Load the malware dataset.

   - Preprocess categorical and numerical features.

2. Train the Malware Classifier:

   - Use the custom neural network to classify the dataset.

   - Train with binary cross-entropy loss.

3. Evaluate the Model:

   - Measure classification metrics (e.g., accuracy, precision).

   - Visualize results with confusion matrices.

4. Discussion:

   - Analyze false positives and negatives in predictions.