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license: other
license_name: brsx-open-license
license_link: https://brsxlabs.gt.tc/brsxlicense.html
tags:
- cybersecurity
- hybrid
pipeline_tag: token-classification
---
# ShadowCore-v1
**Lightweight Network Behavior Classification Model**
ShadowCore-v1 is a compact sequence classification model designed to analyze short network-behavior streams and classify overall system state in real time.
The model operates on a minimal four-token vocabulary representing abstract network activity and can detect healthy, degraded, and potentially dangerous traffic patterns with millisecond-scale inference latency on modern hardware.
---
## Overview
ShadowCore-v1 was created to explore whether a small specialized model can perform behavior-level network analysis without requiring massive LLM-scale architectures.
Instead of processing raw packets, logs, or protocol metadata, ShadowCore-v1 analyzes compressed symbolic sequences that represent network behavior over time.
The model is intended for:
* Network health monitoring
* Congestion detection
* Anomaly detection
* Traffic pattern analysis
* Lightweight edge deployment
* Real-time alerting systems
---
## Vocabulary
ShadowCore-v1 uses a fixed vocabulary of only four tokens:
| Token | Meaning |
| ----- | ----------------------------------- |
| U | Upload activity |
| D | Download activity |
| + | High latency / congestion / waiting |
| - | Processing completed / idle time |
Example:
```text
UU--DD--UU--DD--
```
Interpretation:
```text
Request
↓
Processing
↓
Response
↓
Idle
```
This pattern generally represents healthy behavior.
---
## Classification Labels
### NORMAL
Healthy system state.
Characteristics:
* Upload and download remain balanced
* Few latency spikes
* Stable processing flow
* Idle periods present
Example:
```text
UDUDUDUDUDUDUDUD
```
---
### CRITICAL
System degradation.
Characteristics:
* Upload activity begins exceeding download activity
* Latency clusters appear
* Processing flow becomes unstable
* Queue buildup starts forming
Example:
```text
UUUU++++DDUUUU++++DD
```
---
### DANGER
Potential failure or attack condition.
Characteristics:
* Upload activity dominates
* Download activity becomes rare
* Large latency clusters
* Severe congestion
Example:
```text
UUUUUU++++++++UUUUUU++++++++
```
---
## Input Format
Input length:
```text
64 tokens
```
Example:
```text
UUUU++++DDUUUU++++DDUUUU++++DDUUUU++++DDUUUU++++DDUUUU++++DD
```
Output:
```text
NORMAL
CRITICAL
DANGER
```
---
## Architecture
ShadowCore-v1 is built on the same core architecture family used in previous successful experiments including:
* GenoLite
* IsingBreaker
* ShadowCore
Key design goals:
* Small parameter count
* Fast training
* Fast inference
* Low memory usage
* Strong pattern recognition on symbolic sequences
Model size:
```text
~88 Million Parameters
```
---
## Dataset
Training data was generated using a rule-based synthetic behavior generator.
Dataset characteristics:
```text
4,500 samples
1,500 NORMAL
1,500 CRITICAL
1,500 DANGER
```
Features:
* Fixed-length sequences
* Duplicate filtering
* Motif composition
* Cluster variation
* Sequence rotation
* Behavioral balancing
The generator was designed to teach behavior patterns rather than memorization of exact sequences.
---
## Benchmark Results
Evaluation Accuracy:
```text
94.07%
```
The model consistently identifies:
* Healthy traffic patterns
* Growing congestion states
* Severe overload conditions
Testing also demonstrated reasonable behavior on ambiguous boundary cases, where the model produces mixed confidence instead of collapsing into a single class prediction.
---
## Performance
Training Environment:
```text
NVIDIA T4
Batch Size: 64
Epochs: 5
```
Training Time:
```text
~4.5 minutes
```
Inference:
```text
Millisecond-scale
```
on modern GPUs and suitable for real-time monitoring pipelines.
---
## Example Predictions
Input:
```text
UDUDUDUDUDUDUDUDUDUDUDUDUDUDUDUDUDUDUDUDUDUDUDUDUDUDUDUDUDUDUDUD
```
Prediction:
```text
NORMAL
```
---
Input:
```text
UUUU++++DDUUUU++++DDUUUU++++DDUUUU++++DD
```
Prediction:
```text
CRITICAL
```
---
Input:
```text
UUUUUU++++++++UUUUUU++++++++UUUUUU++++++++
```
Prediction:
```text
DANGER
```
---
## Limitations
ShadowCore-v1 was trained on synthetic data.
While the model successfully learns network-behavior concepts, production deployment should include:
* Real traffic validation
* Domain-specific calibration
* Additional anomaly classes
* Real-world benchmark datasets
---
## Future Work
Planned improvements:
* ShadowCore-v2
* Larger motif library
* Real traffic traces
* Multi-stage anomaly classification
* Attack family detection
* Early-warning forecasting
---
## License
Research & Experimental Use
---
**ShadowCore-v1 demonstrates that lightweight specialized models can achieve >90% accuracy on behavior-oriented sequence classification tasks without requiring large-scale foundation models.** 🚀 |