AI Driven Network Anomaly Detection and Response Workflow Guide

AI-Powered Network Anomaly Detection and Response

1. Data Collection

1.1 Network Traffic Monitoring

Utilize tools such as Wireshark or SolarWinds to capture and analyze network traffic.

1.2 Log Aggregation

Implement solutions like Splunk or ELK Stack to aggregate logs from various network devices for comprehensive analysis.

2. Data Preprocessing

2.1 Data Cleaning

Filter out irrelevant or redundant data to ensure high-quality input for AI models.

2.2 Feature Extraction

Identify key features that may indicate anomalies, such as unusual traffic patterns or unexpected data flows.

3. Anomaly Detection

3.1 AI Model Selection

Choose appropriate AI algorithms such as Random Forest, Support Vector Machines (SVM), or Neural Networks for anomaly detection.

3.2 Tool Implementation

Utilize AI-driven products like Darktrace or Vectra AI to monitor network behavior and detect anomalies in real-time.

4. Incident Response

4.1 Automated Alerts

Configure the AI tools to send automated alerts to network administrators upon detecting anomalies.

4.2 Investigation and Analysis

Leverage forensic tools such as Carbon Black or CrowdStrike to investigate the nature and source of the anomaly.

5. Remediation

5.1 Containment

Implement immediate containment measures to isolate affected systems using tools like Palo Alto Networks or Cisco Secure Firewall.

5.2 Recovery

Restore affected services and systems to normal operation while ensuring that vulnerabilities are addressed.

6. Continuous Improvement

6.1 Feedback Loop

Establish a feedback mechanism to refine AI models based on new data and incident outcomes.

6.2 Regular Updates

Keep AI tools and security protocols updated to adapt to evolving threats and improve detection accuracy.