---
license: mit
tags:
- cybersecurity
- tabular
- tabnet
- network-security
- intrusion-detection
---

# cybersecurity_threat_classifier_tabnet

## Overview
This model utilizes the **TabNet** architecture to perform high-performance classification on tabular network traffic data. It is specifically designed to detect various types of cyber attacks (DDoS, Botnets, etc.) by mimicking the decision-making process of tree-based models while retaining the gradient-based learning advantages of neural networks.



## Model Architecture
The model uses a sequential attention mechanism to focus on the most salient features of a network packet:
- **Feature Transformer**: Processes the input features through shared and independent GLU (Gated Linear Unit) layers.
- **Attentive Transformer**: Learns a sparse mask to select which features the model should "look at" in each decision step.
- **Sparsity Regularization**: Uses entropy-based loss to ensure the model uses a minimal number of features:
$$L_{sparse} = \sum_{i=1}^{N_{steps}} \sum_{j=1}^{D} -M_{i,j} \log(M_{i,j} + \epsilon)$$

## Intended Use
- **IDS/IPS Systems**: Real-time classification of network flows in enterprise firewalls.
- **Forensic Analysis**: Post-hoc analysis of log files to identify patterns of infiltration.
- **Threat Hunting**: Identifying anomalous behavior in high-dimensional telemetry data from zero-trust environments.

## Limitations
- **Feature Engineering**: The model is highly dependent on the quality of input features (e.g., flow duration, packet size variance).
- **Adversarial Attacks**: Highly sophisticated attackers can craft "adversarial traffic" designed to mimic benign flow statistics.
- **Concept Drift**: As new attack vectors emerge, the model requires retraining on updated traffic samples to maintain precision.