AI Enhanced Network Anomaly Detection Workflow for Optimal Security

Intelligent Network Anomaly Detection

1. Data Collection

1.1 Identify Data Sources

Gather data from various network components including routers, switches, firewalls, and servers.

1.2 Utilize AI-Driven Tools

Implement tools such as Splunk and IBM QRadar to automate data collection and log management.

2. Data Preprocessing

2.1 Data Cleaning

Remove duplicates and irrelevant information to ensure data quality.

2.2 Data Normalization

Standardize data formats to facilitate analysis.

3. Anomaly Detection

3.1 Implement Machine Learning Algorithms

Utilize supervised and unsupervised learning techniques to identify anomalies.

Examples of Algorithms:

• Isolation Forest
• Support Vector Machines (SVM)
• Autoencoders

3.2 Use AI-Driven Products

Integrate products like Darktrace and Vectra AI for real-time anomaly detection.

4. Alert Generation

4.1 Define Alert Criteria

Establish thresholds for triggering alerts based on detected anomalies.

4.2 Configure Alerting Systems

Utilize tools like PagerDuty and OpsGenie to manage incident notifications.

5. Incident Response

5.1 Assess Anomalies

Evaluate the severity and potential impact of detected anomalies.

5.2 Execute Response Plans

Implement predefined incident response plans using frameworks such as NIST and MITRE ATT&CK.

6. Continuous Learning and Improvement

6.1 Feedback Loop

Incorporate feedback from incident responses to refine detection algorithms.

6.2 Update AI Models

Regularly retrain AI models with new data to enhance detection accuracy.