Dataset Preparation and Feature Engineering for Cybersecurity Threat Detection

Deep learning models in cybersecurity require large, high-quality datasets to detect threats effectively. The preprocessing stage includes:

• Data Collection: Aggregating logs, network traffic, system events, and user behavior.
• Feature Selection: Identifying key indicators of compromise (IoCs), such as unusual login times, abnormal data transfers, and unauthorized system modifications.
• Data Normalization: Scaling input values to improve training stability.
• One-Hot Encoding: Converting categorical data (e.g., attack types) into numerical format.
• Balancing Datasets: Using techniques like SMOTE (Synthetic Minority Over-sampling Technique) to balance normal vs. attack instances.