Anomaly Detection of Enterprise Web Traffic for a Technology Company

Executive Summary

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.

Algorithm Used: Isolation Forest

Isolation Forest efficiently isolates anomalies through isolation trees. It’s suited for unsupervised tasks as it doesn’t require prior knowledge.

High-dimensional data: Effective in high-dimensional spaces.
Large datasets: Handles large datasets due to its efficient strategy.
Varying densities: Works well with varying density datasets.
Identifying multiple anomalies: Detects multiple anomalies without assuming cluster counts.
Less sensitive to outliers: Robust to outliers.
Easy to implement: User-friendly with fewer hyperparameters.

Training Dataset

A high-quality training dataset is vital. Sources include:

Historical Web Server Logs: Gather logs with normal and anomalous traffic, labeled using intrusion detection or known incidents.
Anomaly Injection: Introduce synthetic anomalies to enhance model detection capability.

Data Cleaning Approach

Data cleaning ensures model accuracy and reliability

Removing Irrelevant Features: Eliminate non-informative features.
Handling Missing Values: Address missing data with imputation or removal.
Data Normalization: Normalize numerical features.
Balancing the Dataset: Counter imbalanced data with techniques like oversampling/undersampling.

Model Training Process

Key steps in training the anomaly detection model:

Data Preprocessing: Clean, transform, and engineer features.
Dataset Splitting: Divide data into training and validation sets.
Model Selection: Choose Isolation Forest or other suitable algorithms.
Model Training: Train the chosen algorithm on the training set.
Model Evaluation: Assess performance using metrics like precision, recall, F1-score, ROC-AUC.
Model Training: Train the chosen algorithm on the training set.
Model Deployment: Deploy in production to monitor real-time traffic.
Ongoing Monitoring and Updates: Continuously monitor and update the model.

Conclusion

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.