AI Driven Network Traffic Analysis and Intrusion Detection Workflow

Intelligent Network Traffic Analysis and Intrusion Detection

1. Data Collection

1.1 Network Traffic Monitoring

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

1.2 Endpoint Data Gathering

Implement agents on endpoints using solutions like CrowdStrike or Carbon Black for real-time data collection.

2. Data Preprocessing

2.1 Data Cleaning

Remove irrelevant or duplicate data entries using Python scripts or ETL tools like Talend.

2.2 Data Normalization

Standardize data formats and structures to ensure consistency across datasets.

3. AI Model Development

3.1 Feature Selection

Identify key features relevant for intrusion detection, such as traffic patterns and user behavior.

3.2 Model Training

Utilize machine learning frameworks like TensorFlow or PyTorch to train models on labeled datasets.

3.3 Model Validation

Evaluate model performance using metrics such as accuracy, precision, and recall.

4. Real-Time Analysis

4.1 Anomaly Detection

Deploy AI-driven anomaly detection tools such as Darktrace or IBM QRadar to identify unusual traffic patterns.

4.2 Threat Intelligence Integration

Incorporate threat intelligence feeds from sources like Recorded Future to enhance detection capabilities.

5. Incident Response

5.1 Automated Response

Utilize SOAR (Security Orchestration, Automation, and Response) platforms like Palo Alto Networks Cortex XSOAR for automated incident response.

5.2 Manual Investigation

Enable security analysts to conduct deeper investigations using tools like Splunk or ELK Stack.

6. Reporting and Compliance

6.1 Generate Reports

Create detailed reports on security incidents and network performance using reporting tools integrated within platforms like ServiceNow.

6.2 Compliance Auditing

Ensure adherence to industry regulations such as NERC CIP by conducting regular audits and assessments.

7. Continuous Improvement

7.1 Feedback Loop

Implement a feedback mechanism to refine AI models based on new threat landscapes and incident outcomes.

7.2 Regular Updates

Continuously update security tools and AI algorithms to adapt to evolving threats and vulnerabilities.