Real Time Anomaly Detection Workflow with AI in Manufacturing

Real-Time Anomaly Detection in Manufacturing Network Traffic

1. Data Collection

1.1 Network Traffic Monitoring

Utilize network monitoring tools to capture real-time data from manufacturing devices and systems. Examples of tools include:

• Wireshark
• Palo Alto Networks
• NetFlow Analyzer

1.2 Data Preprocessing

Clean and preprocess the collected data to remove noise and irrelevant information. This may involve:

• Normalization of data formats
• Filtering out non-essential network packets

2. Feature Engineering

2.1 Identifying Key Features

Extract relevant features from the preprocessed data that can indicate anomalies. Examples include:

• Packet size
• Source and destination IP addresses
• Protocol type

2.2 Transforming Features

Transform the identified features into a format suitable for machine learning algorithms. Techniques may include:

• One-hot encoding for categorical variables
• Scaling numerical values

3. Model Selection

3.1 Choosing the Right AI Model

Select appropriate machine learning models for anomaly detection. Consider using:

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

3.2 Training the Model

Train the selected model using historical network traffic data. Ensure to include both normal and anomalous data for effective learning.

4. Anomaly Detection

4.1 Real-Time Monitoring

Deploy the trained model to monitor network traffic in real-time. Utilize AI-driven security tools such as:

• Darktrace
• Splunk
• Cylance

4.2 Alert Generation

Set up automated alerts for detected anomalies, ensuring timely response. Define thresholds for alerts based on the severity of anomalies.

5. Incident Response

5.1 Investigation

Investigate the nature of detected anomalies to determine if they pose a threat. Utilize forensic analysis tools such as:

• EnCase
• FTK Imager

5.2 Remediation

Implement necessary remediation actions based on the investigation findings. This may include:

• Isolating affected systems
• Applying security patches

6. Continuous Improvement

6.1 Model Retraining

Regularly update and retrain the anomaly detection model with new data to enhance accuracy and performance.

6.2 Feedback Loop

Establish a feedback loop to incorporate lessons learned from incidents into the workflow, improving future anomaly detection capabilities.