first commit

app.py

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+import numpy as np
+import pandas as pd
+from sklearn.preprocessing import StandardScaler
+import joblib
+import gradio as gr
+
+# Load model IDS
+model = joblib.load("model_cb2.pkl")
+
+# Label mapping
+LABEL_CLASS = {
+    0: "Benign",
+    1: "Bot",
+    2: "DDOS attack-HOIC",
+    3: "DDOS attack-LOIC-UDP",
+    4: "DDoS attacks-LOIC-HTTP",        
+    5: "DoS attacks-GoldenEye",
+    6: "DoS attacks-Hulk",
+    7: "DoS attacks-Slowloris",
+    8: "Infilteration",
+    9: "SSH-Bruteforce"
+}
+
+def preprocess_data(df_all):
+    # Hapus kolom tidak penting
+    df_all.drop(columns=['Unnamed: 0', 'Timestamp'], inplace=True, errors='ignore')
+
+    # Ganti inf dengan NaN
+    df_all['Flow Byts/s'].replace([np.inf, -np.inf], np.nan, inplace=True)
+    df_all['Flow Pkts/s'].replace([np.inf, -np.inf], np.nan, inplace=True)
+
+    # Isi NaN
+    df_all['Flow Byts/s'].fillna(df_all['Flow Byts/s'].median(), inplace=True)
+    df_all['Flow Pkts/s'].fillna(df_all['Fwd Pkts/s'] + df_all['Bwd Pkts/s'], inplace=True)
+
+    # Normalisasi
+    df_all = df_all[['Fwd Seg Size Min', 'Bwd IAT Tot', 'Bwd IAT Max', 'Bwd IAT Std',
+       'Bwd IAT Mean', 'PSH Flag Cnt', 'Bwd Pkt Len Min', 'Flow IAT Std',
+       'Flow IAT Max', 'Fwd IAT Std', 'Fwd IAT Max', 'Idle Max', 'Idle Mean',
+       'Bwd IAT Min', 'Bwd Pkt Len Max', 'Idle Min', 'Bwd Pkts/s',
+       'Flow Pkts/s', 'Bwd Seg Size Avg', 'Bwd Pkt Len Mean', 'Fwd PSH Flags',
+       'SYN Flag Cnt', 'Pkt Len Max', 'Fwd Pkt Len Min', 'Pkt Len Min',
+       'ACK Flag Cnt', 'Init Bwd Win Byts', 'Fwd Header Len',
+       'TotLen Fwd Pkts', 'Subflow Fwd Byts', 'Subflow Fwd Pkts',
+       'Tot Fwd Pkts', 'Fwd Act Data Pkts', 'Dst Port', 'ECE Flag Cnt',
+       'RST Flag Cnt']]
+    # scaler = StandardScaler()
+    # df_all[df_all.columns] = scaler.fit_transform(df_all[df_all.columns])
+    
+    return df_all
+
+def predict_ids(file):
+    try:
+        df_all = pd.read_csv(file.name)
+        df_processed = preprocess_data(df_all)
+        pred_indices = model.predict(df_processed)
+
+        # Buat DataFrame hasil
+        hasil = pd.DataFrame({
+            "Index": range(len(pred_indices)),
+            "Predicted Label": [LABEL_CLASS[i] for i in pred_indices.flatten()]
+        })
+
+        return hasil
+    except Exception as e:
+        return f"Terjadi error saat memproses file: {str(e)}"
+
+# Gradio UI
+demo = gr.Interface(
+    fn=predict_ids,
+    inputs=gr.File(label="Upload CSV File", file_types=[".csv"]),
+    outputs=gr.Dataframe(label="Hasil Prediksi (Index dan Label)"),
+    title="Predicting IDS",
+    description="Upload file CSV untuk memprediksi jenis aktivitas jaringan (Benign, BruteForce, dll)"
+)
+
+if __name__ == "__main__":
+    demo.launch()

model_cb2.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:e7ad78f7c3991dd6b0aeaddc6284d6bb6c6679aa30731d312508f646b114f519
+size 5734275

requirements.txt

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+numpy==2.2.4
+pandas==2.2.3
+joblib==1.4.2
+scikit-learn==1.6.1