Use a custom SQL tokenizer

Author: mostafa

Dockerfile

@@ -13,6 +13,8 @@ COPY api/api.py /app
 COPY api/pyproject.toml /app
 COPY api/poetry.lock /app
 COPY dataset/${dataset} /app
+COPY training/sql_tokenizer.py /app/
+COPY training/sql_tokenizer_vocab.json /app/
 COPY sqli_model/ /app/sqli_model/
 RUN pip install --disable-pip-version-check poetry
 RUN poetry install --no-root

api/api.py

@@ -1,9 +1,8 @@
 from flask import Flask, jsonify, request
 import tensorflow as tf
-from tensorflow.keras.preprocessing.sequence import pad_sequences
-from tensorflow.keras.preprocessing.text import Tokenizer
 import pandas as pd
 import os
+from sql_tokenizer import SQLTokenizer  # Import SQLTokenizer
 
 app = Flask(__name__)
 
@@ -12,11 +11,11 @@
 MAX_LEN = 100
 DATASET_PATH = os.getenv("DATASET_PATH", "dataset/sqli_dataset1.csv")
 MODEL_PATH = os.getenv("MODEL_PATH", "/app/sqli_model/3/")
-DATASET = pd.read_csv(DATASET_PATH)
 
-# Tokenizer setup
-TOKENIZER = Tokenizer(num_words=MAX_WORDS, filters="")
-TOKENIZER.fit_on_texts(DATASET["Query"])
+# Load dataset and initialize SQLTokenizer
+DATASET = pd.read_csv(DATASET_PATH)
+sql_tokenizer = SQLTokenizer(max_words=MAX_WORDS, max_len=MAX_LEN)
+sql_tokenizer.fit_on_texts(DATASET["Query"])  # Fit tokenizer on dataset
 
 # Load the model using tf.saved_model.load and get the serving signature
 loaded_model = tf.saved_model.load(MODEL_PATH)
@@ -26,9 +25,8 @@
 def warm_up_model():
     """Sends a dummy request to the model to 'warm it up'."""
     dummy_query = "SELECT * FROM users WHERE id = 1"
-    query_seq = TOKENIZER.texts_to_sequences([dummy_query])
-    query_vec = pad_sequences(query_seq, maxlen=MAX_LEN)
-    input_tensor = tf.convert_to_tensor(query_vec, dtype=tf.float32)
+    query_seq = sql_tokenizer.texts_to_sequences([dummy_query])
+    input_tensor = tf.convert_to_tensor(query_seq, dtype=tf.float32)
     _ = model_predict(input_tensor)  # Make a dummy prediction to initialize the model
     print("Model warmed up and ready to serve requests.")
 
@@ -39,27 +37,26 @@ def predict():
         return jsonify({"error": "No query provided"}), 400
 
     try:
-        # Tokenize and pad the input query
+        # Tokenize and pad the input query using SQLTokenizer
         query = request.json["query"]
-        query_seq = TOKENIZER.texts_to_sequences([query])
-        query_vec = pad_sequences(query_seq, maxlen=MAX_LEN)
-
-        # Convert input to tensor
-        input_tensor = tf.convert_to_tensor(query_vec, dtype=tf.float32)
+        query_seq = sql_tokenizer.texts_to_sequences([query])
+        input_tensor = tf.convert_to_tensor(query_seq, dtype=tf.float32)
 
         # Use the loaded model's serving signature to make the prediction
         prediction = model_predict(input_tensor)
 
+        # Check for valid output and extract the result
         if "output_0" not in prediction or prediction["output_0"].get_shape() != [1, 1]:
             return jsonify({"error": "Invalid model output"}), 500
 
+        # Extract confidence and return the response
         return jsonify(
             {
                 "confidence": float("%.4f" % prediction["output_0"].numpy()[0][0]),
             }
         )
     except Exception as e:
-        # TODO: Log the error and return a proper error message
+        # Log the error and return a proper error message
         return jsonify({"error": str(e)}), 500
 
 

sqli_model/3/fingerprint.pb

@@ -1 +1 @@
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sqli_model/3/saved_model.pb

@@ -0,0 +1,44 @@
+# sql_tokenizer.py
+import re
+import json
+from tensorflow.keras.preprocessing.sequence import pad_sequences
+
+
+class SQLTokenizer:
+    def __init__(self, max_words=10000, max_len=100):
+        self.max_words = max_words
+        self.max_len = max_len
+        self.token_index = {}
+
+    def tokenize(self, query):
+        # Define a regex pattern for SQL tokens (operators, punctuation, keywords)
+        pattern = r"[\w']+|[=><!]+|--|/\*|\*/|;|\(|\)|,|\*|\||\s+"
+        tokens = re.findall(pattern, query.lower())
+        return tokens
+
+    def fit_on_texts(self, queries):
+        # Build a token index based on the provided queries
+        all_tokens = set()
+        for query in queries:
+            tokens = self.tokenize(query)
+            all_tokens.update(tokens)
+        # Limit to max_words
+        all_tokens = list(all_tokens)[: self.max_words]
+        self.token_index = {token: i + 1 for i, token in enumerate(all_tokens)}
+
+    def texts_to_sequences(self, queries):
+        # Convert queries to sequences of token IDs
+        sequences = []
+        for query in queries:
+            tokens = self.tokenize(query)
+            sequence = [self.token_index.get(token, 0) for token in tokens]
+            sequences.append(sequence)
+        return pad_sequences(sequences, maxlen=self.max_len)
+
+    def save_token_index(self, filepath):
+        with open(filepath, "w") as f:
+            json.dump(self.token_index, f)
+
+    def load_token_index(self, filepath):
+        with open(filepath, "r") as f:
+            self.token_index = json.load(f)

sqli_model/3/variables/variables.data-00000-of-00001

@@ -1,26 +1,32 @@
+import os
 import pandas as pd
 import pytest
-from tensorflow.keras.preprocessing.text import Tokenizer
 from tensorflow.keras.preprocessing.sequence import pad_sequences
 from tensorflow.keras.layers import TFSMLayer
-
+from sql_tokenizer import SQLTokenizer
+from tensorflow.keras.preprocessing.text import Tokenizer  # For old tokenizer
 
 MAX_WORDS = 10000
 MAX_LEN = 100
+TOKENIZER_VOCAB_PATH = "sql_tokenizer_vocab.json"  # Path to saved vocabulary
+
 MODELV1 = {
     "dataset": "dataset/sqli_dataset1.csv",
     "model_path": "sqli_model/1",
     "index": 0,
+    "use_sql_tokenizer": False,
 }
 MODELV2 = {
     "dataset": "dataset/sqli_dataset2.csv",
     "model_path": "sqli_model/2",
     "index": 1,
+    "use_sql_tokenizer": False,
 }
 MODELV3 = {
     "dataset": "dataset/sqli_dataset2.csv",
     "model_path": "sqli_model/3",
     "index": 2,
+    "use_sql_tokenizer": True,
 }
 
 
@@ -46,9 +52,23 @@ def model(request):
     model_path = prefix + request.param["model_path"]
     sqli_model = TFSMLayer(model_path, call_endpoint="serving_default")
 
-    # Tokenizer setup
-    tokenizer = Tokenizer(num_words=MAX_WORDS, filters="")
-    tokenizer.fit_on_texts(data["Query"])
+    # Select the appropriate tokenizer
+    if request.param["use_sql_tokenizer"]:
+        # Use SQLTokenizer for MODELV3
+        tokenizer = SQLTokenizer(max_words=MAX_WORDS, max_len=MAX_LEN)
+
+        # Load saved vocabulary if available
+        if os.path.exists(TOKENIZER_VOCAB_PATH):
+            tokenizer.load_token_index(TOKENIZER_VOCAB_PATH)
+        else:
+            tokenizer.fit_on_texts(data["Query"])
+            tokenizer.save_token_index(
+                TOKENIZER_VOCAB_PATH
+            )  # Save for future consistency
+    else:
+        # Use the old Keras Tokenizer for MODELV1 and MODELV2
+        tokenizer = Tokenizer(num_words=MAX_WORDS, filters="")
+        tokenizer.fit_on_texts(data["Query"])
 
     return {
         "tokenizer": tokenizer,
@@ -60,10 +80,10 @@ def model(request):
 @pytest.mark.parametrize(
     "sample",
     [
-        ("select * from users where id=1 or 1=1;", [0.9202, 0.974, 0.0022]),
-        ("select * from users where id='1' or 1=1--", [0.9202, 0.974, 0.0022]),
+        ("select * from users where id=1 or 1=1;", [0.9202, 0.974, 0.3179]),
+        ("select * from users where id='1' or 1=1--", [0.9202, 0.974, 0.3179]),
         ("select * from users", [0.00077, 0.0015, 0.0231]),
-        ("select * from users where id=10000", [0.1483, 0.8893, 0.0008]),
+        ("select * from users where id=10000", [0.1483, 0.8893, 0.7307]),
         ("select '1' union select 'a'; -- -'", [0.9999, 0.9732, 0.0139]),
         (
             "select '' union select 'malicious php code' \\g /var/www/test.php; -- -';",
@@ -76,7 +96,7 @@ def model(request):
     ],
 )
 def test_sqli_model(model, sample):
-    # Vectorize the sample
+    # Tokenize and pad the sample using the selected tokenizer
     sample_seq = model["tokenizer"].texts_to_sequences([sample[0]])
     sample_vec = pad_sequences(sample_seq, maxlen=MAX_LEN)
 
@@ -91,4 +111,5 @@ def test_sqli_model(model, sample):
         f"Predicted: {predicted_value:.4f}, Expected: {sample[1][model['index']]:.4f}"
     )
 
+    # Check that prediction matches expected value within tolerance
     assert predicted_value == pytest.approx(sample[1][model["index"]], abs=0.05)

sqli_model/3/variables/variables.index

@@ -2,8 +2,6 @@
 import os
 import pandas as pd
 import tensorflow as tf
-from tensorflow.keras.preprocessing.text import Tokenizer
-from tensorflow.keras.preprocessing.sequence import pad_sequences
 from tensorflow.keras.models import Sequential
 from tensorflow.keras.layers import (
     Bidirectional,
@@ -21,6 +19,7 @@
 from sklearn.utils.class_weight import compute_class_weight
 import numpy as np
 import matplotlib.pyplot as plt
+from sql_tokenizer import SQLTokenizer
 
 
 def load_data(file_path):
@@ -33,11 +32,11 @@ def load_data(file_path):
 
 
 def preprocess_text(data, max_words=10000, max_len=100):
-    """Tokenize and pad text data."""
-    tokenizer = Tokenizer(num_words=max_words, oov_token="<OOV>")
+    """Tokenize and pad text data using SQLTokenizer."""
+    tokenizer = SQLTokenizer(max_words=max_words)
     tokenizer.fit_on_texts(data["Query"])
     sequences = tokenizer.texts_to_sequences(data["Query"])
-    return pad_sequences(sequences, maxlen=max_len), tokenizer
+    return sequences, tokenizer
 
 
 def build_model(input_dim, output_dim=128):
@@ -97,19 +96,18 @@ def plot_history(history):
 
 if __name__ == "__main__":
     if len(sys.argv) != 3:
-        print("Usage: python train.py <input_file> <output_dir>")
+        print("Usage: python train_v3.py <input_file> <output_dir>")
         sys.exit(1)
 
-    # Constants
     MAX_WORDS = 10000
     MAX_LEN = 100
     EPOCHS = 50
     BATCH_SIZE = 32
 
     # Load and preprocess data
     data = load_data(sys.argv[1])
-    X, tokenizer = preprocess_text(data)
-    y = data["Label"].values  # Convert to NumPy array to avoid KeyError in KFold
+    X, tokenizer = preprocess_text(data, max_words=MAX_WORDS)
+    y = data["Label"].values  # Convert to NumPy array for compatibility with KFold
 
     # Initialize cross-validation
     k_folds = 5
@@ -120,7 +118,7 @@ def plot_history(history):
         print(f"Training fold {fold}/{k_folds}")
 
         # Split the data
-        X_train, X_val = X[train_idx], X[val_idx]
+        X_train, X_val = np.array(X)[train_idx], np.array(X)[val_idx]
         y_train, y_val = y[train_idx], y[val_idx]
 
         # Compute class weights to handle imbalance
@@ -130,7 +128,7 @@ def plot_history(history):
         class_weight_dict = {i: class_weights[i] for i in range(len(class_weights))}
 
         # Build and train the model
-        model = build_model(input_dim=len(tokenizer.word_index) + 1)
+        model = build_model(input_dim=len(tokenizer.token_index) + 1)
         early_stopping = EarlyStopping(
             monitor="val_loss", patience=5, restore_best_weights=True
         )