AI Driven Network Anomaly Detection and Resolution Workflow

Network Anomaly Detection and Resolution

1. Data Collection

1.1 Network Traffic Monitoring

Utilize AI-driven tools such as Cisco’s Stealthwatch or Darktrace to continuously monitor network traffic for anomalies.

1.2 Log Management

Implement solutions like Splunk or ELK Stack to aggregate and analyze logs from various network devices.

2. Anomaly Detection

2.1 Machine Learning Algorithms

Deploy machine learning algorithms to identify unusual patterns in network behavior. Tools like TensorFlow or PyTorch can be utilized for model training.

2.2 Real-time Analysis

Use AI platforms such as IBM Watson or Google Cloud AI to perform real-time analysis of incoming data streams to detect anomalies.

3. Alert Generation

3.1 Automated Alerts

Configure automated alert systems using tools like PagerDuty or OpsGenie to notify network administrators of detected anomalies.

3.2 Severity Assessment

Implement AI-driven risk assessment tools to prioritize alerts based on potential impact, using products like ServiceNow.

4. Investigation and Diagnosis

4.1 Root Cause Analysis

Employ AI analytics platforms, such as Sumo Logic, to assist in conducting root cause analysis of detected anomalies.

4.2 Historical Data Comparison

Utilize historical data for comparison, leveraging data visualization tools like Tableau or Power BI to identify trends and patterns.

5. Resolution

5.1 Automated Remediation

Integrate AI-based automation tools like Ansible or Puppet to automatically remediate identified issues where applicable.

5.2 Manual Intervention

For complex issues, establish a protocol for manual intervention, ensuring that AI tools provide sufficient context and recommendations for network engineers.

6. Post-Incident Review

6.1 Documentation

Document the incident, response actions, and outcomes using knowledge management systems like Confluence or SharePoint.

6.2 Continuous Improvement

Analyze the incident to refine AI models and detection algorithms, ensuring continuous improvement in anomaly detection capabilities.