Real Time Network Traffic Anomaly Detection with AI Integration

Real-Time Network Traffic Anomaly Detection

1. Data Collection

1.1. Network Traffic Monitoring

Utilize tools such as Wireshark or SolarWinds to capture real-time network traffic data.

1.2. Data Aggregation

Aggregate data from various sources including routers, switches, and firewalls using tools like ELK Stack (Elasticsearch, Logstash, Kibana).

2. Data Preprocessing

2.1. Data Cleaning

Remove duplicates, irrelevant data, and noise using Python libraries such as Pandas.

2.2. Feature Selection

Identify relevant features that contribute to anomaly detection, such as packet size, source/destination IP addresses, and protocol types.

3. Anomaly Detection Model Development

3.1. Model Selection

Choose appropriate machine learning algorithms such as Isolation Forest, One-Class SVM, or Neural Networks.

3.2. Training the Model

Utilize AI frameworks like TensorFlow or PyTorch to train the model on historical traffic data, ensuring to include both normal and anomalous patterns.

4. Real-Time Anomaly Detection

4.1. Implementation of AI Algorithms

Deploy the trained model to monitor live network traffic using platforms like Apache Kafka for real-time data streaming.

4.2. Alert Generation

Set up alerting mechanisms using tools such as PagerDuty or OpsGenie to notify network administrators of detected anomalies.

5. Post-Detection Analysis

5.1. Incident Response

Implement a response plan that includes investigation and remediation steps for detected anomalies.

5.2. Continuous Improvement

Regularly update the model with new data and feedback to improve detection accuracy using tools like MLflow for model management.

6. Reporting and Documentation

6.1. Reporting Tools

Utilize BI tools such as Tableau or Power BI to create dashboards that visualize network traffic and anomalies.

6.2. Documentation

Maintain comprehensive documentation of the workflow, model performance, and incident responses for compliance and future reference.