AI Powered Real Time Network Anomaly Detection Workflow Guide

Real-Time Network Anomaly Detection Portal

1. Data Collection

1.1 Source Identification

Identify key data sources within the telecommunications network, including:

• Network traffic logs
• Device performance metrics
• Customer usage patterns

1.2 Data Ingestion

Utilize tools such as Apache Kafka or AWS Kinesis for real-time data ingestion.

2. Data Preprocessing

2.1 Data Cleaning

Implement data cleaning techniques to remove noise and irrelevant information using Python libraries such as Pandas.

2.2 Feature Engineering

Extract relevant features that could indicate anomalies, including:

• Latency spikes
• Unusual traffic patterns
• Device error rates

3. Anomaly Detection Model Development

3.1 Model Selection

Select appropriate AI-driven algorithms for anomaly detection, such as:

• Isolation Forest
• Autoencoders
• Long Short-Term Memory (LSTM) networks

3.2 Model Training

Train the selected models using historical data to establish baseline performance metrics.

3.3 Model Evaluation

Evaluate model performance using metrics such as precision, recall, and F1-score.

4. Real-Time Monitoring

4.1 Deployment

Deploy the trained model into a production environment using platforms like TensorFlow Serving or AWS SageMaker.

4.2 Continuous Monitoring

Implement a monitoring dashboard using tools like Grafana or Kibana to visualize real-time data and alert on anomalies.

5. Incident Response

5.1 Alerting Mechanism

Set up automated alerts via email or SMS using services like Twilio or PagerDuty when anomalies are detected.

5.2 Investigation Protocol

Establish a protocol for incident investigation, including:

• Assigning a response team
• Documenting the incident
• Implementing corrective actions

6. Feedback Loop

6.1 Model Retraining

Regularly retrain the model with new data to improve accuracy and adapt to changing network conditions.

6.2 Performance Review

Conduct periodic reviews of the anomaly detection system to assess performance and make necessary adjustments.