STEG-DEF leverages advanced machine learning algorithms specialized in detecting patterns and anomalies in traffic flow and packet sizes instead of examining the content directly.

It uses deep learning models trained on large datasets of encrypted traffic to identify subtle traffic signature deviations which suggest manipulation typical of covert communications.

This approach uses probabilistic models to focus on traffic behavior rather than content, integrating with existing cybersecurity frameworks to alert analysts to potential threats for further investigation.

The system respects encryption and works within privacy regulations by not analyzing or decrypting actual content, but rather examining traffic metrics and metadata legally accessible under current law.

STEG-DEF offers an unprecedented ability to identify covert communications in encrypted streams without breaching privacy laws, thus filling a critical gap in national security infrastructure.

It reduces false positives common with current passive metadata analysis, providing a higher fidelity of threat detection and supporting legal compliance.

The necessary technology for traffic pattern recognition exists, but models require substantial training data and computational resources to ensure high accuracy.

Costs involve robust AI infrastructure and compliance with Eurozone privacy regulations.

Competitors may include established encryption and cybersecurity firms; however, the unique angle of not decrypting data while maintaining legality provides a significant differentiation.