Behavioral Analysis v5.0

Model Scoring

The Model Scoring tier executes ensemble machine learning classifiers to produce high-fidelity anomaly scores. It operates at the P2 layer of the arbitration hierarchy.

Inference Logic

quorum-scoring.py

# ENSEMBLE INFERENCE PIPELINE
def execute_inference(feature_vector):
    # 1. Load Optimized Model Graph
    model = MLRuntime.load("quorum_v5_ensemble")

    # 2. Execute Multi-Head Classifiers
    anomaly_p = model.predict_anomaly(feature_vector)
    velocity_p = model.predict_velocity(feature_vector)
    entropy_p = model.predict_behavioral_entropy(feature_vector)

    # 3. Resolve Composite Score (P2 Layer)
    composite_score = (anomaly_p * 0.5) + (velocity_p * 0.3) + (entropy_p * 0.2)
    
    # 4. Return Normalized Prediction [0, 1]
    return {
        "score": composite_score,
        "confidence": model.confidence_interval(),
        "version": "5.0.4-release"
    }

Scoring Specs

Ensemble Logic

Combines Gradient Boosting, Neural Networks, and Isolation Forests to minimize false-positive variance.

Behavioral Entropy

Analyzes the "chaos" level of session interactions to identify non-human navigational patterns.

GPU Acceleration

Inference is executed on dedicated Tensor cores to ensure model latency remains below 5ms.