AI Driven Adaptive Network Anomaly Detection Workflow Guide

Adaptive Network Anomaly Detection System

1. Data Collection

1.1 Identify Data Sources

Collect data from various sources including:

• Network traffic logs
• System performance metrics
• User activity logs
• Threat intelligence feeds

1.2 Data Ingestion

Utilize tools such as:

• Apache Kafka for real-time data streaming
• Logstash for log data collection

2. Data Preprocessing

2.1 Data Cleaning

Implement algorithms to identify and remove noise and irrelevant data.

2.2 Feature Extraction

Utilize techniques such as:

• Principal Component Analysis (PCA)
• Statistical analysis for identifying significant features

3. Anomaly Detection Model Development

3.1 Model Selection

Choose appropriate AI-driven models such as:

• Isolation Forest for outlier detection
• Autoencoders for unsupervised anomaly detection

3.2 Training the Model

Utilize frameworks like:

• TensorFlow for building neural networks
• Scikit-learn for traditional machine learning algorithms

4. Model Evaluation

4.1 Performance Metrics

Evaluate model performance using metrics such as:

• Precision and Recall
• F1 Score
• ROC-AUC

4.2 Validation Techniques

Implement cross-validation to ensure model robustness.

5. Deployment

5.1 Integration into Existing Systems

Utilize APIs for seamless integration with:

• SIEM tools (e.g., Splunk, IBM QRadar)
• Network monitoring systems

5.2 Continuous Monitoring

Deploy the model in a production environment to monitor network traffic in real-time.

6. Feedback Loop

6.1 Anomaly Reporting

Set up automated alerts for detected anomalies.

6.2 Model Retraining

Regularly update the model with new data to improve accuracy and adapt to evolving threats.

7. Documentation and Compliance

7.1 Maintain Documentation

Document all processes, model parameters, and decision-making protocols.

7.2 Compliance Checks

Ensure adherence to regulations such as GDPR and CCPA in data handling practices.