T-IO
Open console
Neural valve pipeline visualization of network and binary signal flows
analytical twin · clone agent · SaaS

An analytical twin of every packet and every binary you touch.

T-IO clones the behavior of your network and your executables into one model. Four valves extract the signal, an LSTM remembers the sequence, PCA and t-SNE compress the space, and a Random Forest calls the verdict.

Run the twin See the valve theory
4
feature valves
2
pipelines
LSTM
sequence core
RF
classifier head

The four valves

Each valve is an independent tap on the sample. The same theory maps onto packets and onto instructions — identity, structure, substrate, temporality.

V1

Identity

Traffic pipeline

IP addresses & port numbers

Binary pipeline

Instruction opcodes & operands

V2

Structure

Traffic pipeline

Packet length, protocol & flags (TCP SYN)

Binary pipeline

Control flow: jumps, branches, loops

V3

Substrate

Traffic pipeline

Source & destination MAC addresses

Binary pipeline

Data structures & memory access patterns

V4

Temporality

Traffic pipeline

Timestamps & inter-arrival times

Binary pipeline

System calls & API usage

Twin console

Trace a sample end to end: valve extraction, LSTM embedding, dimensionality reduction and forest scoring with per-valve attribution.

T-IO twin console
V1

Identity

IP addresses & port numbers

5 feat
src_ipdst_ipsrc_portdst_portport_entropy
V2

Structure

Packet length, protocol & flags (TCP SYN)

5 feat
pkt_lenprototcp_flagssyn_ratiowin_size
V3

Substrate

Source & destination MAC addresses

4 feat
src_macdst_macoui_vendormac_churn
V4

Temporality

Timestamps & inter-arrival times

5 feat
ts_deltaiat_meaniat_varburstinessflow_dur
  1. 01

    Capture

    PCAP ingest / sandbox detonation

  2. 02

    4-Valve extraction

    Parallel feature taps

  3. 03

    LSTM sequencer

    Temporal pattern embedding

  4. 04

    PCA / t-SNE

    Dimensionality reduction

  5. 05

    Random Forest

    Malware verdict + confidence

Run the twin to trace a sample through the full valve pipeline.

Capabilities

Sequence memory

LSTM layers learn packet-length and inter-packet-time sequences, and opcode/syscall sequences from detonated samples.

Valve fusion

Four orthogonal feature taps are concatenated, normalized and fused into one twin state vector per sample.

PCA / t-SNE

Collapse thousands of sparse features into a dense, separable manifold you can actually inspect and cluster.

Random Forest verdicts

An ensemble classifier scores the reduced space and returns per-valve attribution for every decision.

Dual pipelines

Live traffic capture and sandbox binary analysis run the same valve theory over different substrates.

Drift tracking

The twin re-scores historical samples as it learns, exposing model drift before it costs you a detection.