NFC Security Using Smart Cyber Contracts in Zero-Trust Networks

The system performs preliminary actions by training the anomaly detection engine and generating smart cyber contracts before actual NFC transactions occur. Historical user information is processed in advance to establish baseline behavior patterns, and security policies are pre-configured to automatically evaluate and block suspicious transactions before they can compromise sensitive information.

The patent introduces an intermediary anomaly detection engine that sits between the NFC communication channels and the target systems. This engine acts as a mediator that intercepts, analyzes, and evaluates transaction data using machine learning models, allowing legitimate fast NFC transactions to proceed while blocking malicious ones without affecting the underlying NFC technology's speed.

The anomaly detection engine serves multiple functions simultaneously: it trains on historical data to learn patterns, evaluates real-time transactions for anomalies, generates smart cyber contracts with security policies, and updates its models continuously. This multi-functionality consolidates what could be separate complex systems into a single unified platform, reducing overall system complexity while maintaining high security reliability.

The system implements self-service through automated machine learning processes where the anomaly detection engine continuously trains itself on new data, automatically updates its anomaly detection models, and dynamically generates updated smart cyber contracts without requiring manual intervention. This self-updating capability reduces operational complexity while enhancing security over time.

The system applies partial action by performing full anomaly analysis only when necessary rather than on every transaction. The anomaly detection engine uses confidence thresholds to determine when detailed analysis is needed versus when transactions can be processed with standard validation. This selective approach maintains high authentication reliability for suspicious transactions while minimizing processing time delays for legitimate transactions.

The system implements feedback mechanisms where the anomaly detection engine continuously learns from transaction outcomes and updates its models accordingly. Successful transactions provide positive feedback that refines the understanding of normal behavior patterns, while blocked transactions provide negative feedback that improves detection accuracy. This continuous learning reduces false positives over time, decreasing unnecessary processing delays while maintaining security.