On-Device Network TinyML Engine V4

AI Network Threat & Anomaly Detection

Standard firewalls only match static signatures. Malloc deploys quantized neural networks directly to device edge hardware to detect zero-day spyware, autonomous AI agent exfiltration, rogue beacons, and telemetry anomalies in real time—completely offline and zero-knowledge.

How Network AI Works Deployment Domains
Behavioral Traffic Analysis
<1.2ms Inference
Zero Payload Inspection
MALLOC_NETWORK_AI // INFERENCE_ACTIVE
100% LOCAL
// Active Network Feature Extraction
flow_entropy: 0.8419 (Normal)
burst_interval: 12ms
packet_size_stddev: 42.8 bytes
NETWORK ANOMALY DETECTED [ID: 0x89F]
Pattern: Uncharacteristic agent exfiltration / C2 beacon.
Action: Socket terminated locally (Zero Cloud Delay).
Memory: 11.2 MB RAM Execution: Native ARM Neon / NPU
Network Architecture

On-Device Edge Network Pipeline

Local socket metadata extraction isolates malicious traffic before packets can leave the hardware boundary.

On-Device Network AI Threat Detection Pipeline Architecture flow illustrating Socket Metadata extraction, TinyML Inference, Behavioral Scoring, and Autonomous Socket Isolation on the local device. 1. Socket Metadata Packet sizes & inter-arrival timing metrics LOCAL NPU EXECUTION 2. TinyML Inference Latency < 1.2ms INT8 Quantized Neural Model 3. Behavioral Scoring Identifies stealth C2 beacons & agent exfiltration 4. Block Local Sever
Intelligence at the Edge

The On-Device Network Anomaly Pipeline

Instead of inspecting plain text or decrypting private traffic, Malloc analyzes structural network metadata using trained neural networks.

01

Network Metadata

Evaluates packet sizes, inter-arrival timing, burst durations, and socket state frequencies without accessing private payload content.

02

TinyML Traffic Inference

Quantized INT8 neural networks run inferences on network flows in sub-millisecond intervals directly on modern edge NPU cores.

03

Behavioral Scoring

Traffic is compared against baseline normal network behavior to flag stealth exfiltration, agent hijacking, unauthorized telemetry, and C2 beacons.

04

Autonomous Isolation

Offending network sockets are severed locally at the network stack before malicious data can exit the physical endpoint hardware.

Multi-Domain Deployment Architecture

A single lightweight network threat engine optimized across mobile devices, autonomous software agents, industrial hardware, and tactical defense networks.

Mobile Endpoints

Protects iOS and Android devices against commercial spyware, unauthorized background network calls, and rogue VPN routing.

  • Detects zero-click spyware activity
  • Preserves battery via NPU acceleration
  • 100% private on-device processing

AI Agents & Autonomous Systems

Guards autonomous agentic runtimes, local LLM tools, and robotics from prompt-injection network exfiltration and unauthorized API calls.

  • Blocks prompt injection exfiltration
  • Enforces runtime socket guardrails
  • Zero-trust data leak prevention

IoT Edge Gateways

Prevents industrial gateways, smart city hardware, and connected sensors from being hijacked into botnets or leaking facility telemetry.

  • Runs on micro-controllers (<16MB RAM)
  • Blocks Mirai-style botnet recruitment
  • Compiled for ARM Cortex-M & RISC-V

Combat Cloud

Delivers autonomous threat detection to tactical units and autonomous platforms operating under RF silence or electronic warfare jamming.

  • Operates in 100% disconnected state
  • Flags covert spectrum emissions
  • Pairs with Post-Quantum Mesh Tunnels

Cloud Inspection vs. Malloc On-Device Network AI

Traditional network security routes your traffic metadata to remote clouds for inspection. Malloc brings the neural network model directly to your hardware, securing end-user mobile devices, autonomous agents, and edge gateways locally at the network stack.

Request Network AI Benchmark Paper
Feature Cloud Traffic Feeds Malloc Edge Network AI
Privacy Guarantee Traffic logs sent to external cloud 100% Local (Zero Transfer)
Offline Availability Fails when network drops Fully Operational Offline
Detection Latency 100ms - 2000ms (Network delay) < 1.2ms (Zero Cloud Lag)
Zero-Day Anomaly Detection Limited to known IP/DNS signatures Behavioral Machine Learning

Deploy On-Device Network Threat Detection Today

Experience Malloc's network threat engine in our consumer mobile app, or talk to our technical team regarding AI Agent, IoT, and Defense SDK integrations.

Download Malloc Security App on Google Play Store Download Malloc Security App on Apple App Store Contact Engineering