NetSense — Research & Methodology

Research

AI Methodology &
Model Architecture

NetSense's intelligence layer is built on peer-reviewed research in network anomaly detection, causal inference on temporal graphs, and safe reinforcement learning for autonomous systems.

FOUNDATION MODEL

NetFormer Architecture

Custom transformer variant optimized for heterogeneous network telemetry sequences. Multi-scale temporal attention with sparse connectivity patterns mirrors the hierarchical structure of real network topologies. Pre-trained on 14.2 trillion anonymized network events spanning 6 years of global internet telemetry.

Parameters: 12.8B (full) · 340M (edge-distilled)
Context window: 32,768 timesteps
Attention: sparse local + global routing tokens
Pre-training: masked telemetry modeling + next-event prediction

CAUSAL INFERENCE

Temporal Causal Discovery

Graph Neural Network operating on the live topology graph performs continuous causal structure learning. Identifies root cause propagation paths through multi-hop dependency chains using Granger causality on learned latent representations, combined with intervention-based validation in the digital twin.

GNN: 6-layer message-passing with edge-typed attention
Causal discovery: NOTEARS + temporal extensions
Validation: counterfactual simulation in digital twin
Root cause latency: P50 87ms · P99 194ms

SAFE RL

Constrained Policy Optimization

Remediation agent trained via Constrained Policy Optimization (CPO) with formal safety guarantees. The action space is bounded by operator-defined invariants verified through SMT solvers. Every candidate action is simulated in a high-fidelity network twin before live execution, with automatic rollback on constraint violation.

Algorithm: CPO with Lagrangian relaxation
Safety verification: Z3 SMT solver for invariant checking
Simulation fidelity: 99.4% topology accuracy
Rollback latency: <200ms full state restoration

BENCHMARK RESULTS

Performance Against Industry Baselines

99.997%

Anomaly detection precision (F1)

vs. 94.2% — threshold-based systems
vs. 97.8% — prior SOTA (DeepAnT)

4-6 hrs

Predictive lead time for outages

vs. 15-30 min — rule-based prediction
vs. 1-2 hrs — prior ML approaches

91.3%

Root cause accuracy (top-1)

vs. 62% — correlation-based RCA
vs. 78% — graph-based RCA (MicroCause)

Safety constraint violations (12mo)

142,847 autonomous remediations executed
Zero rollbacks due to safety violations

PROTOCOL COVERAGE

Full-Stack Telemetry Ingestion

Layer 2–3

BGP (eBGP/iBGP)
OSPF / IS-IS
MPLS / SR-MPLS
SRv6 / EVPN
VXLAN / GRE / IPsec
ARP / NDP / LLDP

Layer 4–7

TCP state machine analysis
QUIC / HTTP/3 telemetry
TLS 1.3 handshake profiling
DNS (DoH / DoT / recursive)
gRPC / WebSocket / REST
MQTT / AMQP / Kafka

Cloud & SDN

AWS VPC Flow Logs
Azure NSG / GCP VPC
Kubernetes CNI (Cilium/Calico)
Service Mesh (Istio/Linkerd)
OpenConfig / gNMI streaming
NETCONF / RESTCONF

Integrations