Next-generation Intrusion Detection Systems (IDS) powered by Graph Neural Networks (GNNs) and Muon Real-Time Gateway.
Traditional IDS flags anomalies but doesn't tell you why. Our platform integrates Muon to ingest real-time IoT data via MQTT/Kafka, uses GNNs to model complex node relationships, and layers an integrated IDS & IPS Prevention System to surface weighted evidence for every alert and automatically queue containment actions.
Maps network topology to identify suspicious node correlations (e.g., botnet rings) that standard rule-based systems miss.
Seamlessly ingests data from millions of edge devices using lightweight protocols (MQTT) for instant analysis.
Generates human-readable incident reports instantly, explaining technical anomalies in plain language.
The combined IDS/IPS layer ties every anomalous path to underlying telemetry, scores prevention confidence, maps affected assets, and can trigger automated isolation or throttling so SOC teams can justify and enforce responses.
"High-confidence alert (98%): Node #442 is exhibiting lateral movement consistent with Mirai Botnet patterns. Correlated with unauthorized MQTT publish attempts."
Hard numbers behind the Muon + GNN defense stack: how we ingest, correlate, explain, and enforce inside critical IoT and grid environments.
Layered ST-GCN models with attention pooling preserve temporal and topological context on every hop.
Muon Gateway normalizes multi-protocol device traffic before it touches the model surface.
Coordinated IDS/IPS actions flow into OT control systems without waiting for manual runbooks.
Maps every device identity and IP segment to graph context so detections become enforceable in-network.
Built for regulated operators that demand provable control over data, models, and audit trails.
Domain-tuned large language models translate telemetry spikes into investigator-ready narratives.