Anomaly Detection of Enterprise Web Traffic for a Technology Company
This case study explores how AI/ML techniques enhanced web infrastructure security through anomaly detection
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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.
Feature Engineering Techniques
Time-Based Features
Extract temporal aspects like timestamp, day,
and hour to capture periodic trends
GeoIP Information
Use GeoIP to pinpoint request origins
Traffic Rate
Calculate request rates to identify spikes or drops
User Agent Analysis
Parse user-agents
to detect device/
browser types
Session Analysis
Detects changes in session duration, frequency, and activity
Request Metadata
Include HTTP method, response codes, request size, and URL components for insights into requests
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.
