feat: AI based traffic classification experiment

classifier/classifier.py

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+import tensorflow as tf
+import numpy as np
+
+# Sample Data
+data = [
+    [200, 20, 0.1, 0.05, 0.0],  # normal traffic
+    [404, 30, 0.2, 0.05, 0.0],  # normal traffic
+    [500, 40, 0.5, 0.1, 0.3],   # malicious traffic
+    [200, 25, 0.1, 0.05, 0.2],  # advertisement traffic
+    [200, 22, 0.1, 0.05, 0.0],  # normal traffic
+    [500, 45, 0.6, 0.2, 0.4]    # malicious traffic
+]
+
+# Corresponding Labels
+labels = [0, 0, 1, 2, 0, 1]  # 0: normal, 1: malicious, 2: advertisement
+
+data = np.array(data)
+labels = np.array(labels)
+
+model = tf.keras.models.Sequential([
+    tf.keras.layers.Dense(128, activation='relu',
+                          input_shape=(data.shape[1],)),
+    tf.keras.layers.Dropout(0.2),
+    tf.keras.layers.Dense(64, activation='relu'),
+    tf.keras.layers.Dropout(0.2),
+    # Assume 10 classes for classification
+    tf.keras.layers.Dense(10, activation='softmax')
+])
+
+model.compile(optimizer='adam',
+              loss='sparse_categorical_crossentropy',
+              metrics=['accuracy'])
+
+model.fit(data, labels, epochs=5)
+
+model.save('traffic_classifier')
+
+
+def classify(data):
+    model = tf.keras.models.load_model('my_model')
+    # processed_data = data_preprocessing(data)
+    predictions = model.predict(data)
+    return np.argmax(predictions, axis=1)  # Returns the class labels
+
+
+# Assume new_data is the new HTTP response data you receive in real-time
+# new_data = ...
+classifications = classify(data)
+
+print(classifications)
+
+
+# Assume data_preprocessing is a function that preprocesses your raw data
+# Assume label_data is a function that labels your data
+# raw_data would be your collected HTTP response data
+
+# raw_data = ...
+# data = data_preprocessing(raw_data)
+# labels = label_data(raw_data)

classifier/traffic_classifier/fingerprint.pb

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classifier/traffic_classifier/keras_metadata.pb

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