Anomaly detection is crucial for identifying unusual and potentially malicious activities in a technology company's web traffic. This case study explores how AI/ML techniques enhanced web infrastructure security through anomaly detection. We focus on feature engineering, the algorithm used, training data, and data cleaning.
Isolation Forest efficiently isolates anomalies through isolation trees. It's suited for unsupervised tasks as it doesn't require prior knowledge.
A high-quality training dataset is vital. Sources include:
Data cleaning ensures model accuracy and reliability.
Key steps in training the anomaly detection model:
Applying AI/ML for anomaly detection enhances cybersecurity. Effective feature engineering combined with Isolation Forest detects threats efficiently. A curated training dataset and robust data cleaning ensured a reliable model safeguarding web infrastructure against malicious activities.
