Real Time AI Driven Network Anomaly Detection Workflow Guide

Real-Time Network Anomaly Detection Using AI

1. Data Collection

1.1 Network Traffic Monitoring

Utilize tools like Wireshark or SolarWinds to capture network traffic data.

1.2 Log Data Aggregation

Implement Splunk or ELK Stack to aggregate logs from various sources for comprehensive analysis.

2. Data Preprocessing

2.1 Data Cleaning

Remove irrelevant or corrupted data using Python libraries such as Pandas.

2.2 Feature Engineering

Extract relevant features from the data, such as packet size and protocol type, to enhance model accuracy.

3. Anomaly Detection Model Development

3.1 Selection of AI Algorithms

Choose appropriate algorithms like Isolation Forest or Autoencoders for anomaly detection.

3.2 Model Training

Train the selected models using historical network data to identify patterns and anomalies.

4. Real-Time Monitoring

4.1 Deployment of AI Models

Deploy models using platforms such as AWS SageMaker or Google AI Platform for scalable performance.

4.2 Continuous Data Ingestion

Set up a pipeline using Apache Kafka to continuously feed real-time data into the model.

5. Anomaly Detection and Alerting

5.1 Real-Time Anomaly Detection

Utilize the trained AI models to analyze incoming network traffic and identify anomalies.

5.2 Alert Generation

Integrate with PagerDuty or Opsgenie for automatic alert notifications to the cybersecurity team.

6. Response and Mitigation

6.1 Incident Response Plan Activation

Follow the predefined incident response plan to address detected anomalies promptly.

6.2 Post-Incident Analysis

Conduct a thorough analysis of the incident using tools like Maltego for future prevention.

7. Continuous Improvement

7.1 Model Retraining

Regularly retrain AI models with new data to improve accuracy and adapt to evolving threats.

7.2 Feedback Loop Implementation

Establish a feedback mechanism to refine anomaly detection processes based on incident outcomes.