AI Driven DNS Traffic Monitoring and Threat Detection Workflow

Intelligent DNS Traffic Monitoring and Threat Identification

1. Data Collection

1.1 DNS Query Logging

Utilize logging tools such as Wireshark or tcpdump to capture DNS queries and responses.

1.2 Traffic Analysis

Implement network monitoring solutions like SolarWinds or NetFlow Analyzer to analyze traffic patterns.

2. AI Integration

2.1 Machine Learning Model Development

Develop machine learning models using platforms like TensorFlow or PyTorch to categorize DNS queries based on historical data.

2.2 Anomaly Detection

Deploy AI-driven anomaly detection tools such as Darktrace or Splunk to identify deviations from normal traffic behavior.

3. Threat Identification

3.1 Signature-Based Detection

Utilize threat intelligence platforms like Recorded Future or ThreatConnect for known malicious signatures.

3.2 Heuristic Analysis

Implement heuristic analysis tools such as CrowdStrike or McAfee to identify potentially harmful behavior in DNS queries.

4. Incident Response

4.1 Automated Response

Use automated incident response tools like Demisto or ServiceNow to initiate predefined actions upon threat detection.

4.2 Manual Investigation

Conduct manual investigations using forensic tools such as FTK Imager or EnCase for deeper analysis.

5. Reporting and Feedback Loop

5.1 Generate Reports

Create detailed reports using business intelligence tools like Tableau or Power BI to visualize DNS traffic and threats.

5.2 Continuous Improvement

Incorporate feedback into machine learning models to enhance detection capabilities and adapt to evolving threats.