Add timestamp to synthetic data for accurate model testing

Add a 'timestamp' column to the synthetic dataset generation in `python_ml/dataset_loader.py` to resolve a `KeyError` during model training and testing.

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attached_assets/Pasted-python-train-hybrid-py-test-WARNING-Extended-Isolation-Forest-not-available-using-standard-IF--1764006677142_1764006677142.txt

@@ -0,0 +1,54 @@
+python train_hybrid.py --test
+[WARNING] Extended Isolation Forest not available, using standard IF
+
+======================================================================
+  IDS HYBRID ML TEST - SYNTHETIC DATA
+======================================================================
+INFO:dataset_loader:Creating sample dataset (10000 samples)...
+INFO:dataset_loader:Sample dataset created: 10000 rows
+INFO:dataset_loader:Attack distribution:
+attack_type
+normal         8981
+brute_force     273
+suspicious      258
+ddos            257
+port_scan       231
+Name: count, dtype: int64
+
+[TEST] Created synthetic dataset: 10000 samples
+  Normal:  8,981 (89.8%)
+  Attacks: 1,019 (10.2%)
+
+[TEST] Training on 6,281 normal samples...
+[HYBRID] Training hybrid model on 6281 logs...
+
+❌ Error: 'timestamp'
+Traceback (most recent call last):
+  File "/opt/ids/python_ml/venv/lib64/python3.11/site-packages/pandas/core/indexes/base.py", line 3790, in get_loc
+    return self._engine.get_loc(casted_key)
+           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "index.pyx", line 152, in pandas._libs.index.IndexEngine.get_loc
+  File "index.pyx", line 181, in pandas._libs.index.IndexEngine.get_loc
+  File "pandas/_libs/hashtable_class_helper.pxi", line 7080, in pandas._libs.hashtable.PyObjectHashTable.get_item
+  File "pandas/_libs/hashtable_class_helper.pxi", line 7088, in pandas._libs.hashtable.PyObjectHashTable.get_item
+KeyError: 'timestamp'
+
+The above exception was the direct cause of the following exception:
+
+Traceback (most recent call last):
+  File "/opt/ids/python_ml/train_hybrid.py", line 361, in main
+    test_on_synthetic(args)
+  File "/opt/ids/python_ml/train_hybrid.py", line 249, in test_on_synthetic
+    detector.train_unsupervised(normal_train)
+  File "/opt/ids/python_ml/ml_hybrid_detector.py", line 204, in train_unsupervised
+    features_df = self.extract_features(logs_df)
+                  ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/opt/ids/python_ml/ml_hybrid_detector.py", line 98, in extract_features
+    logs_df['timestamp'] = pd.to_datetime(logs_df['timestamp'])
+                                          ~~~~~~~^^^^^^^^^^^^^
+  File "/opt/ids/python_ml/venv/lib64/python3.11/site-packages/pandas/core/frame.py", line 3893, in __getitem__
+    indexer = self.columns.get_loc(key)
+              ^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/opt/ids/python_ml/venv/lib64/python3.11/site-packages/pandas/core/indexes/base.py", line 3797, in get_loc
+    raise KeyError(key) from err
+KeyError: 'timestamp'

python_ml/dataset_loader.py

@@ -364,6 +364,17 @@ Expected files:
         unique_ips = [f"192.168.{i//256}.{i%256}" for i in range(100)]
         data['source_ip'] = np.random.choice(unique_ips, n_samples)
         
+        # Add timestamp column (simulate last 7 days of traffic)
+        from datetime import datetime, timedelta
+        now = datetime.now()
+        start_time = now - timedelta(days=7)
+        
+        # Generate timestamps randomly distributed over last 7 days
+        time_range_seconds = 7 * 24 * 3600  # 7 days in seconds
+        random_offsets = np.random.uniform(0, time_range_seconds, n_samples)
+        timestamps = [start_time + timedelta(seconds=offset) for offset in random_offsets]
+        data['timestamp'] = timestamps
+        
         df = pd.DataFrame(data)
         
         # Make attacks more extreme