# Cybercrime LSTM + GloVe Model

This model is a Long Short-Term Memory (LSTM) model trained with GloVe embeddings for classifying cybercrime categories. It has been trained on various cybercrime data and aims to provide high accuracy in detecting and categorizing different cybercrime types.

## Model Details
- **Model Type**: LSTM
- **Embeddings**: GloVe
- **Categories**: Offensive, botnet, DDoS, ransomware, vulnerability, non-cybercrime, etc.

## Usage
This model can be used for cybercrime classification tasks. 

Accuracy: 0.9803

Precision: 0.9804

Recall: 0.9803

F1 Score: 0.9803

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  df_org['label'] = df_org['label'].replace('unknown', 'not cybercrime')  # Replace 'unknown' with 'not cybercrime'
<ipython-input-6-8f9ee34c78b4>:37: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.
  df_balanced = df_org.groupby('label', group_keys=False).apply(lambda x: x.sample(max_samples, replace=True))
  
Epoch 1/10

158/158 [==============================] - 65s 316ms/step - loss: 1.4255 - accuracy: 0.5900 - val_loss: 0.9644 - val_accuracy: 0.8066

Epoch 2/10
158/158 [==============================] - 73s 461ms/step - loss: 0.6081 - accuracy: 0.8742 - val_loss: 0.3353 - val_accuracy: 0.9132

Epoch 3/10
158/158 [==============================] - 50s 316ms/step - loss: 0.2752 - accuracy: 0.9344 - val_loss: 0.1922 - val_accuracy: 0.9534

Epoch 4/10
158/158 [==============================] - 59s 376ms/step - loss: 0.1848 - accuracy: 0.9563 - val_loss: 0.1487 - val_accuracy: 0.9664

Epoch 5/10
158/158 [==============================] - 66s 419ms/step - loss: 0.1423 - accuracy: 0.9676 - val_loss: 0.1272 - val_accuracy: 0.9714

Epoch 6/10
158/158 [==============================] - 64s 408ms/step - loss: 0.1176 - accuracy: 0.9722 - val_loss: 0.1133 - val_accuracy: 0.9745

Epoch 7/10
158/158 [==============================] - 67s 422ms/step - loss: 0.0971 - accuracy: 0.9789 - val_loss: 0.1042 - val_accuracy: 0.9749

Epoch 8/10
158/158 [==============================] - 76s 479ms/step - loss: 0.0814 - accuracy: 0.9818 - val_loss: 0.0910 - val_accuracy: 0.9794

Epoch 9/10
158/158 [==============================] - 51s 324ms/step - loss: 0.0727 - accuracy: 0.9859 - val_loss: 0.0862 - val_accuracy: 0.9799

Epoch 10/10
158/158 [==============================] - 41s 260ms/step - loss: 0.0638 - accuracy: 0.9864 - val_loss: 0.0791 - val_accuracy: 0.9803