AI Driven Network Anomaly Detection Workflow for Enhanced Security

AI-Driven Network Anomaly Detection

1. Data Collection

1.1 Network Traffic Monitoring

Utilize tools such as Wireshark and SolarWinds to capture and analyze network traffic data.

1.2 Log Data Aggregation

Implement Splunk or ELK Stack to aggregate logs from various sources including servers, firewalls, and intrusion detection systems.

2. Data Preprocessing

2.1 Data Cleaning

Remove irrelevant data and normalize formats to ensure consistency across datasets.

2.2 Feature Extraction

Utilize Python libraries such as Pandas and NumPy to extract relevant features from the collected data.

3. Anomaly Detection Model Development

3.1 Selection of AI Algorithms

Choose appropriate machine learning algorithms such as Isolation Forest, Support Vector Machines (SVM), or Deep Learning models like Autoencoders.

3.2 Model Training

Utilize platforms like TensorFlow or PyTorch to train the selected models on historical data.

4. Model Evaluation

4.1 Performance Metrics

Evaluate model performance using metrics such as Precision, Recall, and F1 Score.

4.2 Cross-Validation

Implement k-fold cross-validation to ensure the model’s robustness and generalizability.

5. Deployment

5.1 Integration with Existing Systems

Integrate the anomaly detection model with existing cybersecurity infrastructure using APIs.

5.2 Continuous Monitoring

Utilize tools like Prometheus or Grafana for real-time monitoring of network activity and model performance.

6. Incident Response

6.1 Alert Generation

Configure automated alerts through platforms like PagerDuty or OpsGenie for detected anomalies.

6.2 Investigation and Remediation

Establish a protocol for incident investigation, utilizing tools such as Carbon Black or CrowdStrike for threat analysis.

7. Continuous Improvement

7.1 Feedback Loop

Implement a feedback mechanism to refine the model based on new data and evolving threats.

7.2 Regular Updates

Schedule periodic reviews and updates of the anomaly detection model to adapt to changing network environments.