Briefing
Neural Threat Engine

AI-Driven Threat Detection

SecureFlow uses deep learning to analyze unidirectional traffic. Select a model architecture to inspect real-time packet inference and threat detection metrics.

INPUT SHAPE
(B, 128, 1)
IP Header Stream
LATENCY
0.8 ms (FP16)
Real-time Edge
FLOP LOAD
8.2 GFLOPs
Optimized Flow
ACCURACY
98.5% Top-1
Threat Detection

Detection Pipeline

STAGE 01

Traffic Ingestion

(B, 128, 1, 1024)

Captures unidirectional IP packet headers into fixed-size temporal buffers for deep inspection.

Stride: s=1, Window: T=128, Normalized: mean=[0.5, 0.5]
STAGE 02

Spatial Feature Map

(B, 128, 256, 32)

Applies 1D convolutional layers to identify malicious byte-pattern signatures in packet payloads.

Global pool transforms volume into (B, 128, 256) vector
STAGE 03

Temporal LSTM Core

(B, 128, 512)

Models packet sequence dynamics to detect anomalies in unidirectional traffic flow patterns.

Hidden units: 256 per direction (512 total), Dropout: 0.2
STAGE 04

Threat Classification

(B, 8) Threat Types

Multi-head attention identifies specific threat vectors and triggers real-time security alerts.

Temperature: T=1.05, Focal Loss optimized
cyberai_lstm_threat.py
PyTorch 2.3+
# CYBERAI CNN-LSTM Threat Pipeline
import torch
import torch.nn as nn
from cyberai.models import TrafficPipeline

model = TrafficPipeline(
    backbone="resnet1d_v2",
    spatial_pretrained=true,
    temporal_engine="bi_lstm",
    hidden_dim=256,
    num_layers=2,
    bidirectional=true,
    num_classes=8,
    dropout=0.2,
    precision="fp16"
)

# Input: Batch=8, Packets=128, Channels=1
input_tensor = torch.randn(8, 128, 1, 1024, device="cuda", dtype=torch.float16)
threats, attention = model(input_tensor)
# Output: (8, 8) threat confidence

Engineering Notes

Parameters for traffic resolution, gradient stability, and packet inspection mechanics.

Traffic subsampling uses dynamic windows: high-bandwidth streams apply s=1, while low-latency monitoring uses s=2 to optimize throughput.

Need custom models?

Read the integration handbook.

View Docs
Threat Detection Engine

Algorithmic Pipeline

Advanced AI-based detection for unidirectional IP traffic. We safeguard critical infrastructure through deep learning and real-time analytics.

Real-Time
VEC-INSP-01
Vectorized Inspection
Deep Packet Inspection (DPI)

Unidirectional traffic analysis that identifies malicious patterns without altering the physical data flow.

  • Hardware-level packet stream monitoring
  • Zero-latency threat signature matching
  • Non-intrusive traffic flow validation
Predictive
VEC-INSP-02
Heuristic Detection
Neural Anomaly Inference

Advanced deep learning models that detect zero-day threats by analyzing behavioral deviations in traffic.

  • Baseline traffic pattern modeling
  • Automated anomaly vector isolation
  • Continuous threat intelligence updates
Verifiable
VEC-INSP-03
Telemetry Integration
SOC-Ready Data Streams

Seamless integration with existing security operations centers to provide actionable, high-fidelity alerts.

  • Standardized SIEM log export formats
  • Real-time incident response triggers
  • Comprehensive network health metrics
Hardened
VEC-INSP-04
Infrastructure Shield
Air-Gapped Compliance

Designed for critical infrastructure, ensuring maximum security without compromising network performance.

  • NERC-CIP compliance ready architecture
  • Hardware-enforced unidirectional diodes
  • Minimal impact on throughput latency