Real Time Network Anomaly Detection with AI Integration

Real-Time Network Anomaly Detection and Response

1. Data Collection

1.1 Network Traffic Monitoring

Utilize tools such as Wireshark or SolarWinds to capture real-time network traffic data.

1.2 Log Aggregation

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

2. Data Preprocessing

2.1 Data Cleansing

Remove irrelevant or redundant data using Python scripts or tools like Talend.

2.2 Feature Engineering

Identify key features relevant to anomaly detection, such as unusual traffic patterns or login attempts.

3. Anomaly Detection

3.1 AI Model Selection

Select appropriate AI models for anomaly detection, such as:

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

3.2 Implementation of AI Tools

Utilize AI-driven products such as:

• Darktrace for unsupervised machine learning in real-time threat detection.
• IBM QRadar for integrating AI with security analytics.

4. Anomaly Analysis

4.1 Automated Alerts

Set up automated alert systems to notify security teams of detected anomalies.

4.2 Root Cause Analysis

Conduct thorough investigations using tools like CrowdStrike to determine the source and nature of the anomaly.

5. Response Actions

5.1 Incident Classification

Classify incidents based on severity and potential impact on the organization.

5.2 Automated Response

Implement automated response mechanisms using SOAR (Security Orchestration, Automation, and Response) tools like Palo Alto Networks Cortex XSOAR.

6. Continuous Improvement

6.1 Feedback Loop

Integrate feedback from incident responses to refine AI models and detection algorithms.

6.2 Regular Updates

Ensure continuous updates of AI tools and threat intelligence databases to adapt to evolving threats.