AI Driven Network Anomaly Detection Workflow for Enhanced Security

AI-Driven Network Anomaly Detection

1. Data Collection

1.1 Identify Data Sources

Gather data from various sources including:

• Network traffic logs
• User activity logs
• System performance metrics

1.2 Tools for Data Collection

Utilize tools such as:

• Wireshark for packet analysis
• Splunk for log management
• ELK Stack (Elasticsearch, Logstash, Kibana) for data aggregation

2. Data Preprocessing

2.1 Data Cleaning

Remove duplicates and irrelevant data to ensure accuracy.

2.2 Feature Engineering

Identify and extract relevant features that may indicate anomalies, such as:

• Unusual login times
• Abnormal data transfer volumes

3. Model Selection

3.1 Choose AI Algorithms

Select appropriate machine learning algorithms for anomaly detection, such as:

• Isolation Forest
• Support Vector Machines (SVM)
• Neural Networks

3.2 Tools for Model Development

Implement models using platforms like:

• TensorFlow
• PyTorch
• Scikit-learn

4. Model Training

4.1 Training the Model

Utilize historical data to train the selected model, ensuring it learns to identify normal behavior.

4.2 Hyperparameter Tuning

Optimize model performance by adjusting hyperparameters.

5. Anomaly Detection

5.1 Real-time Monitoring

Deploy the trained model to monitor network traffic in real-time.

5.2 Detection of Anomalies

Utilize the model to flag any deviations from normal behavior.

6. Response and Mitigation

6.1 Alert Generation

Generate alerts for detected anomalies and notify relevant stakeholders.

6.2 Incident Response

Implement predefined incident response protocols to address detected anomalies.

7. Continuous Improvement

7.1 Feedback Loop

Incorporate feedback from incident responses to improve the model’s accuracy and reduce false positives.

7.2 Model Retraining

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