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: $$L_{sparse}=\sum _{i=1}^{N_{steps}}\sum _{j=1}^D-M_{i,j}\log (M_{i,j}+ϵ)L\_\{sparse\}\; =\; \backslash sum\_\{i=1\}^\{N\_\{steps\}\}\; \backslash sum\_\{j=1\}^\{D\}\; -M\_\{i,j\}\; \backslash log(M\_\{i,j\}\; +\; \backslash epsilon)$$

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.