AI Driven Network Traffic Analysis and Anomaly Detection Workflow

Intelligent Network Traffic Analysis and Anomaly Detection

1. Data Collection

1.1 Network Traffic Monitoring

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

1.2 Data Aggregation

Aggregate data from various sources including routers, switches, and firewalls using a centralized logging solution like Splunk or ELK Stack.

2. Data Preprocessing

2.1 Data Cleaning

Implement data cleaning techniques to remove duplicates and irrelevant information using Python libraries such as Pandas.

2.2 Data Normalization

Normalize data to ensure consistency across datasets, preparing it for analysis using tools like Apache NiFi.

3. Feature Engineering

3.1 Selection of Key Features

Identify relevant features that contribute to network behavior analysis, such as packet size, source/destination IP addresses, and protocols.

3.2 Creation of New Features

Generate new features that may indicate anomalies, such as the frequency of requests or unusual traffic patterns using machine learning techniques.

4. Anomaly Detection

4.1 Implementation of AI Algorithms

Deploy machine learning algorithms such as Isolation Forest, Random Forest, or Neural Networks to identify anomalies in network traffic.

4.2 Use of AI-Driven Tools

Utilize AI-driven products like Darktrace or Vectra AI that leverage machine learning for real-time anomaly detection.

5. Alerting and Reporting

5.1 Automated Alert System

Set up an automated alert system to notify cybersecurity teams of detected anomalies via platforms like PagerDuty or OpsGenie.

5.2 Reporting Dashboard

Create a reporting dashboard using visualization tools such as Tableau or Grafana to present insights and trends in network traffic.

6. Investigation and Response

6.1 Incident Investigation

Conduct thorough investigations of flagged anomalies using forensic tools like EnCase or FTK.

6.2 Response Protocols

Implement response protocols based on the severity of the anomaly, utilizing incident response platforms like ServiceNow or IBM Resilient.

7. Continuous Improvement

7.1 Feedback Loop

Establish a feedback loop to refine AI models based on incident outcomes and changing network behaviors.

7.2 Regular Updates

Regularly update detection algorithms and tools to adapt to new threats and vulnerabilities in the cybersecurity landscape.