Implement Random Forest Algorithm for Encrypted Traffic Classification

[mmanoj]

@IvanNardi As per our initial discussion:

Is your feature request related to a problem? Please describe.
Detecting malware and covert communications within encrypted traffic, especially when anonymized through software like VPNs, presents significant challenges. Traditional deep packet inspection techniques are often ineffective due to encryption, necessitating the adoption of advanced AI and machine learning (ML) algorithms for effective analysis. Implementing the Random Forest algorithm within the nDPI (nDPI) framework can enhance the classification of encrypted traffic, enabling more accurate detection of malicious patterns. Future integration of optimization algorithms aims to further improve classification accuracy and expand the detection of emerging threat patterns.​

Research indicates that the Random Forest algorithm is particularly effective in classifying encrypted traffic. For instance, a study demonstrated that Random Forest achieved an F1-score of 99% in distinguishing VPN-encrypted from non-VPN traffic, highlighting its robustness in handling complex, encrypted data. Additionally, integrating Random Forest with frameworks like deep forests has shown promise in detecting SSL/TLS-encrypted malicious traffic, even with small-scale and unbalanced training datasets. ​

Describe the solution you'd like

By embedding the Random Forest algorithm into the nDPI framework, we can enhance the infrastructure's capability to analyze encrypted traffic more effectively. This integration will facilitate the identification of covert channels and malware communications that traditional methods might overlook. Furthermore, incorporating optimization algorithms will refine the classification process, improving accuracy and enabling the detection system to adapt to evolving threat landscapes.

Describe alternatives you've considered
Add optimization algorithms to enhance feature selection.

Additional context

Some reference materials:

https://arxiv.org/abs/2502.13804?utm_source=chatgpt.com

https://www.mdpi.com/2079-9292/11/7/977?utm_source=chatgpt.com