Upload 6 files

label_encoder.joblib

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+oid sha256:e85e8150b11b1cb1427578abea1ea4bf17dfff23a3ad701bc0f7ddd8f91db1cc
+size 507

model.joblib

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 version https://git-lfs.github.com/spec/v1
-oid sha256:27dfde6db68fa8cabdb5ddaec8257b094c6d03d921103446cd1a113dea00bdfc
-size 8236641
+oid sha256:dd5e3179a6b0ccd2bbb8a63d58459ab436254c8d88a60eb2e26e68c5f98205b4
+size 43222057

retrain.py

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+"""
+retrain.py — Firewall Log Classifier
+Trains the Random Forest model on Dataset__log2_.csv and saves:
+  - model.joblib
+  - scaler.joblib
+  - label_encoder.joblib
+
+Run:  python retrain.py
+Requires: pip install scikit-learn imbalanced-learn pandas joblib
+"""
+
+import os
+import pandas as pd
+import numpy as np
+import joblib
+from sklearn.ensemble import RandomForestClassifier
+from sklearn.preprocessing import LabelEncoder, StandardScaler
+from sklearn.model_selection import train_test_split
+from sklearn.metrics import accuracy_score, f1_score, classification_report
+from imblearn.over_sampling import SMOTE
+
+# ── Config ────────────────────────────────────────────────────────────────────
+DATASET_PATH = "Dataset__log2_.csv"
+
+FEATURE_COLS = [
+    "Source Port",
+    "Destination Port",
+    "NAT Source Port",
+    "NAT Destination Port",
+    "Bytes",
+    "Bytes Sent",
+    "Bytes Received",
+    "Packets",
+    "Elapsed Time (sec)",
+    "pkts_sent",
+    "pkts_received",
+]
+
+TARGET_COL = "Action"
+
+OUTPUT_DIR = os.path.dirname(os.path.abspath(__file__))
+
+# ── 1. Load ───────────────────────────────────────────────────────────────────
+print(f"Loading {DATASET_PATH} ...")
+df = pd.read_csv(DATASET_PATH)
+print(f"  Raw rows: {len(df):,}   Columns: {list(df.columns)}")
+
+# ── 2. Verify columns exist ───────────────────────────────────────────────────
+missing = [c for c in FEATURE_COLS + [TARGET_COL] if c not in df.columns]
+if missing:
+    print("\nERROR — these columns are missing from the CSV:")
+    for m in missing:
+        print(f"  '{m}'")
+    print("\nAvailable columns:", list(df.columns))
+    raise SystemExit(1)
+
+# ── 3. Preprocessing ──────────────────────────────────────────────────────────
+df.drop_duplicates(inplace=True)
+print(f"  After dedup: {len(df):,} rows")
+# IQR filtering skipped — it eliminates the rare reset-both class entirely.
+# SMOTE handles class imbalance instead.
+
+X = df[FEATURE_COLS].values
+y = df[TARGET_COL].values
+
+print(f"  Class distribution:\n{pd.Series(y).value_counts().to_string()}")
+
+# ── 4. Encode labels ──────────────────────────────────────────────────────────
+le = LabelEncoder()
+y_enc = le.fit_transform(y)
+print(f"  Classes: {list(le.classes_)}")
+
+# ── 5. Train / test split ─────────────────────────────────────────────────────
+X_train, X_test, y_train, y_test = train_test_split(
+    X, y_enc, test_size=0.30, stratify=y_enc, random_state=42
+)
+print(f"  Train: {len(X_train):,}   Test: {len(X_test):,}")
+
+# ── 6. SMOTE ──────────────────────────────────────────────────────────────────
+print("Applying SMOTE ...")
+sm = SMOTE(random_state=42)
+X_train_res, y_train_res = sm.fit_resample(X_train, y_train)
+print(f"  After SMOTE train size: {len(X_train_res):,}")
+
+# ── 7. Scale ──────────────────────────────────────────────────────────────────
+scaler = StandardScaler()
+X_train_sc = scaler.fit_transform(X_train_res)
+X_test_sc  = scaler.transform(X_test)
+
+# ── 8. Train tuned Random Forest ──────────────────────────────────────────────
+print("Training Random Forest (n_estimators=200, max_depth=20) ...")
+rf = RandomForestClassifier(
+    n_estimators=200,
+    max_depth=20,
+    min_samples_split=2,
+    random_state=42,
+    n_jobs=-1,
+)
+rf.fit(X_train_sc, y_train_res)
+
+# ── 9. Evaluate ───────────────────────────────────────────────────────────────
+y_pred = rf.predict(X_test_sc)
+acc  = accuracy_score(y_test, y_pred)
+mf1  = f1_score(y_test, y_pred, average="macro")
+print(f"\nTest Accuracy : {acc*100:.2f}%")
+print(f"Macro F1      : {mf1:.4f}")
+print("\nClassification Report:")
+print(classification_report(y_test, y_pred, target_names=le.classes_))
+
+# ── 10. Save artifacts ───��────────────────────────────────────────────────────
+model_path   = os.path.join(OUTPUT_DIR, "model.joblib")
+scaler_path  = os.path.join(OUTPUT_DIR, "scaler.joblib")
+le_path      = os.path.join(OUTPUT_DIR, "label_encoder.joblib")
+
+joblib.dump(rf,     model_path)
+joblib.dump(scaler, scaler_path)
+joblib.dump(le,     le_path)
+
+print(f"\nSaved:")
+print(f"  {model_path}")
+print(f"  {scaler_path}")
+print(f"  {le_path}")
+print("\nDone! Upload these 3 files to your repo alongside app.py.")

scaler.joblib

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+oid sha256:d52e80c3f5709cb89dd6358602bdf5574ed6fe350d5f0504c7e8d0ecd7314f68
+size 831