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--- |
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license: mit |
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tags: |
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- cybersecurity |
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- tabular |
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- tabnet |
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- network-security |
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- intrusion-detection |
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--- |
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# cybersecurity_threat_classifier_tabnet |
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## Overview |
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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. |
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## Model Architecture |
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The model uses a sequential attention mechanism to focus on the most salient features of a network packet: |
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- **Feature Transformer**: Processes the input features through shared and independent GLU (Gated Linear Unit) layers. |
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- **Attentive Transformer**: Learns a sparse mask to select which features the model should "look at" in each decision step. |
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- **Sparsity Regularization**: Uses entropy-based loss to ensure the model uses a minimal number of features: |
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$$L_{sparse} = \sum_{i=1}^{N_{steps}} \sum_{j=1}^{D} -M_{i,j} \log(M_{i,j} + \epsilon)$$ |
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## Intended Use |
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- **IDS/IPS Systems**: Real-time classification of network flows in enterprise firewalls. |
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- **Forensic Analysis**: Post-hoc analysis of log files to identify patterns of infiltration. |
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- **Threat Hunting**: Identifying anomalous behavior in high-dimensional telemetry data from zero-trust environments. |
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## Limitations |
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- **Feature Engineering**: The model is highly dependent on the quality of input features (e.g., flow duration, packet size variance). |
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- **Adversarial Attacks**: Highly sophisticated attackers can craft "adversarial traffic" designed to mimic benign flow statistics. |
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- **Concept Drift**: As new attack vectors emerge, the model requires retraining on updated traffic samples to maintain precision. |
