Qognet AI
The Qognet AI is the central cognitive engine of the ecosystem. It utilizes advanced Graph Neural Networks (GNNs) to analyze transaction flows and detect malicious patterns in real-time.
Key Capabilities
- Predictive Analysis: Anticipates attacks before they are confirmed on-chain.
- Behavioral Modeling: Understands the intent behind transactions rather than just static code analysis.
- Real-Time Processing: Operates at the speed of the blockchain to provide instant security decisions.
How It Works
Unlike traditional security tools that rely on known signatures, Qognet AI learns from the "Threat Genome" to identify zero-day exploits by recognizing anomalous behavioral sequences in the mempool.
Qognet Safety Score (QSS)
The Qognet Safety Score (QSS) is a dynamic risk rating assigned to transactions and addresses. It serves as the primary signal for the Qognet BioFirewall to accept or block interactions.
Scoring Mechanism
The QSS is calculated based on multiple factors:
- Historical Behavior: Past interactions and reputation.
- Transaction Simulation: Predicted outcome of the transaction.
- Graph Analysis: Relationships with known malicious entities.
Risk Levels
- High QSS (Safe): Transaction proceeds normally.
- Medium QSS (Caution): Additional verification may be required.
- Low QSS (Danger): Transaction is automatically blocked by the BioFirewall.