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Unsupervised learning provides Neuroarcane with a complementary analytic dimension to the supervised and deep-learning systems described in our previous blogs. While regression, classification, and neural networks excel when labeled ground truth is available, a significant portion of global internet interference occurs without clear annotations: shutdowns unfold without declared timestamps, throttling escalates covertly, DNS manipulation is localized, and BGP anomalies propag

Neural networks (or deep learning) enable Neuroarcane to move beyond the linear boundaries of classical supervised learning and model the complex, nonlinear dynamics inherent in global internet interference. While regression and logistic classification provide strong first-order signals about drift and tampering, as demonstrated in our previous blog , deep learning allows us to capture higher-order patterns embedded within multi-dimensional, multi-source measurement streams c

Supervised Learning at Neuroarcane   Supervised learning offers a powerful lens through which Neuroarcane interprets, forecasts, and classifies patterns in global internet behavior. Internet interference—whether caused by throttling, DNS tampering, protocol blocking, or targeted shutdowns—manifests through measurable distortions in the underlying traffic. Datasets such as OONI, IODA, CAIDA Ark, and RIPE Atlas provide continuous measurements of latency, blocking signatures, an