Real Time Network Anomaly Detection with AI Integration Workflow

Real-Time Network Anomaly Detection

1. Data Collection

1.1 Network Traffic Monitoring

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

1.2 Log Aggregation

Implement Splunk or ELK Stack to aggregate logs from various network devices and servers for centralized analysis.

2. Data Preprocessing

2.1 Data Cleaning

Remove duplicates, irrelevant data, and noise using Python libraries like Pandas.

2.2 Feature Engineering

Extract relevant features from the raw data to improve model accuracy, utilizing techniques such as Principal Component Analysis (PCA).

3. Anomaly Detection Model Development

3.1 Model Selection

Choose appropriate AI algorithms such as Isolation Forest, Autoencoders, or Support Vector Machines (SVM).

3.2 Training the Model

Train the model using labeled datasets with tools such as TensorFlow or Scikit-learn.

4. Real-Time Analysis

4.1 Deployment of AI Models

Deploy the trained models in a production environment using AWS SageMaker or Azure Machine Learning.

4.2 Continuous Monitoring

Implement real-time monitoring solutions like Prometheus to track the performance of the deployed models.

5. Anomaly Detection and Alerting

5.1 Anomaly Detection

Utilize the deployed model to analyze incoming network traffic and identify anomalies.

5.2 Alert Generation

Configure alerting mechanisms using PagerDuty or Slack to notify security teams of detected anomalies.

6. Incident Response

6.1 Investigation

Conduct a thorough investigation of detected anomalies using forensic tools like FTK Imager or EnCase.

6.2 Remediation

Implement remediation steps based on findings, which may include isolating affected systems or applying patches.

7. Feedback Loop

7.1 Model Retraining

Regularly update the anomaly detection model with new data to enhance its accuracy and adapt to evolving threats.

7.2 Performance Evaluation

Continuously evaluate the model’s performance using metrics such as Precision, Recall, and F1 Score to ensure effectiveness.