Cyberdroid Neural Engine learns how every user, host, process, application, and relationship normally behaves — then catches the spikes, silences, drifts, new edges, and campaign patterns that rule-only stacks miss.
Time-series ML, peer-group analytics, deep-learning reconstruction, graph relationship models, threat intelligence, and deterministic detections — one evidence pipeline. Instead of asking analysts to trust a black-box alert, the engine shows the model, the baseline, the entity, the relationship, and the supporting evidence.
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Neural UEBA learns per-entity behavior across users, hosts, applications, processes, and hourly features — then watches it with multiple independent anomaly families instead of a single opaque score.
Atlas is the graph intelligence layer. It builds a persistent entity graph — hosts, users, processes, network endpoints, authentication activity, observed security events — and treats relationship change as detection signal, not decoration.
An anomaly is not enough. Threat Horizon connects ML findings, threat intel, rule detections, and correlated attack-chain metadata into timelines, evidence chains, campaign views, and graph context — so analysts understand why a signal fired, what changed, and what to do next.
Every algorithm has a stable ID, a defined objective, required telemetry, and a documented evidence product — disclosed in a client-safe catalog. Procurement, audit, and detection engineering can all answer the same question: "which algorithms are you using?"
The library spans time-series baselines, change & drift, outlier & density, deep learning, sequence & language, graph, and fusion analytics — roughly 90 named algorithm families, with the 200+ figure counting variants and research-grid combinations under an explicit counting rule.
The full, named inventory ships in the client-safe catalog, and tier badges publish the Forward Flight Plan — production today, staged next, research beyond. Nobody has to guess.
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A dedicated research cluster trains, fuses, and validates new algorithm combinations against production-shaped telemetry before staged rollout — from detector fusion to graph neural scoring with explainability guardrails. Nothing ships until it survives contact with real operations: online computation, bounded memory, explainable evidence, and safe failure under drift.
The library is designed to grow with customers. Every new entry follows a public template — objective, telemetry, method, evidence product, operational notes — and co-development is part of the offer.
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Cyberdroid Neural Engine was not assembled in a funding cycle. More than ten years of research and engineering stand behind it — the patented and patent-pending prediction, wavelet, and temporal-quantization families among them. Every instrument in the library earned its place.
