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6f3b14e | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 | from fastapi import APIRouter, UploadFile, File, HTTPException
from typing import Dict
import pandas as pd
import io
import os
import tempfile
from routers.predict import model, predict_with_model, ATTACK_MAP
# from utils.pcap_converter import convert_pcap_to_csv # Temporarily commented
import numpy as np
router = APIRouter()
# @router.post("/convert-pcap")
# async def convert_pcap(file: UploadFile = File(...)):
# """
# Convert uploaded PCAP file to CSV and return it as a download
# """
# try:
# filename = file.filename.lower()
# if not (filename.endswith('.pcap') or filename.endswith('.pcapng')):
# raise HTTPException(status_code=400, detail="Only .pcap or .pcapng files are allowed")
#
# # Save PCAP to temp file
# with tempfile.NamedTemporaryFile(delete=False, suffix=os.path.splitext(filename)[1]) as tmp:
# content = await file.read()
# tmp.write(content)
# tmp_path = tmp.name
#
# try:
# # Convert to DataFrame
# df = convert_pcap_to_csv(tmp_path)
#
# # Convert to CSV string
# stream = io.StringIO()
# df.to_csv(stream, index=False)
# response = stream.getvalue()
#
# # Return as file
# from fastapi.responses import Response
# return Response(
# content=response,
# media_type="text/csv",
# headers={"Content-Disposition": f"attachment; filename={filename}.csv"}
# )
#
# finally:
# # Cleanup temp file
# if os.path.exists(tmp_path):
# os.remove(tmp_path)
#
# except Exception as e:
# import traceback
# print(traceback.format_exc())
# raise HTTPException(status_code=500, detail=f"Error converting file: {str(e)}")
@router.post("/upload")
async def analyze_csv(file: UploadFile = File(...)):
"""
Upload a CSV file and get analysis similar to dashboard stats
"""
try:
# Validate file type
if not file.filename.endswith('.csv'):
raise HTTPException(status_code=400, detail="Only CSV files are allowed")
# Read CSV file
contents = await file.read()
df = pd.read_csv(io.StringIO(contents.decode('utf-8')))
# Limit to 100,000 rows for performance
if len(df) > 100000:
df = df.head(100000)
# Validate required columns
required_cols = ['Protocol', 'Total Fwd Packets', 'Total Backward Packets']
missing_cols = [col for col in required_cols if col not in df.columns]
if missing_cols:
raise HTTPException(
status_code=400,
detail=f"CSV is missing required columns: {', '.join(missing_cols)}"
)
# Filter out non-feature columns
feature_cols = [col for col in df.columns if col not in ['Attack_type', 'Attack_encode']]
X = df[feature_cols]
# Handle NaN values
X = X.fillna(0)
# Predict
if model:
preds = predict_with_model(model, X)
pred_labels = [int(p) if isinstance(p, (int, float, np.number)) else int(p) for p in preds]
pred_names = [ATTACK_MAP.get(p, 'Unknown') for p in pred_labels]
else:
raise HTTPException(status_code=503, detail="Model not loaded")
# Calculate statistics
total_flows = len(df)
# Attack Distribution
attack_counts = {}
for name in pred_names:
attack_counts[name] = attack_counts.get(name, 0) + 1
# Protocol Distribution (All)
protocol_counts = {}
if 'Protocol' in df.columns:
protocol_counts = df['Protocol'].value_counts().head(10).to_dict()
# Protocol Distribution (Malicious)
malicious_protocol_counts = {}
recent_threats = []
# Create temporary dataframe with predictions
temp_df = df.copy()
temp_df['Predicted_Attack'] = pred_names
malicious_df = temp_df[temp_df['Predicted_Attack'] != 'Benign']
if not malicious_df.empty:
if 'Protocol' in malicious_df.columns:
malicious_protocol_counts = malicious_df['Protocol'].value_counts().head(10).to_dict()
# Recent Threats (last 20)
threats_df = malicious_df.tail(20).iloc[::-1]
for idx, row in threats_df.iterrows():
recent_threats.append({
"id": int(idx),
"attack": row['Predicted_Attack'],
"protocol": str(row['Protocol']) if 'Protocol' in row else "Unknown",
"severity": "High",
"fwd_packets": int(row.get('Total Fwd Packets', 0)),
"bwd_packets": int(row.get('Total Backward Packets', 0))
})
return {
"success": True,
"filename": file.filename,
"total_flows": total_flows,
"attack_counts": attack_counts,
"protocol_counts": protocol_counts,
"malicious_protocol_counts": malicious_protocol_counts,
"recent_threats": recent_threats
}
except pd.errors.EmptyDataError:
raise HTTPException(status_code=400, detail="CSV file is empty")
except pd.errors.ParserError:
raise HTTPException(status_code=400, detail="Invalid CSV format")
except Exception as e:
import traceback
print(traceback.format_exc())
raise HTTPException(status_code=500, detail=f"Error processing file: {str(e)}")
@router.post("/feature-importance")
async def calculate_feature_importance(file: UploadFile = File(...)):
"""
Calculate feature importance (SHAP values) for uploaded CSV file
"""
try:
# Validate file type
if not file.filename.endswith('.csv'):
raise HTTPException(status_code=400, detail="Only CSV files are allowed")
# Read CSV file
contents = await file.read()
df = pd.read_csv(io.StringIO(contents.decode('utf-8')))
# Limit to 10,000 rows for SHAP calculation (performance)
if len(df) > 10000:
df = df.head(10000)
# Filter out non-feature columns
feature_cols = [col for col in df.columns if col not in ['Attack_type', 'Attack_encode']]
X = df[feature_cols]
# Handle NaN values
X = X.fillna(0)
# Get feature importance from model
if model:
try:
feature_names = X.columns.tolist()
print(f"Model type: {type(model)}")
print(f"Model attributes: {dir(model)}")
# Try to get feature importances using different methods
importances = {}
# Method 1: Try feature_importances_ attribute (sklearn-style)
if hasattr(model, 'feature_importances_'):
print("Using feature_importances_ attribute")
importances = dict(zip(feature_names, model.feature_importances_.tolist()))
print(f"Got {len(importances)} importances, sample: {list(importances.items())[:3]}")
# Method 2: Try get_score for XGBoost Booster
elif hasattr(model, 'get_score'):
print("Using get_score method")
# Try different importance types
for importance_type in ['weight', 'gain', 'cover']:
try:
importance_dict = model.get_score(importance_type=importance_type)
print(f"get_score({importance_type}): {list(importance_dict.items())[:3] if importance_dict else 'empty'}")
if importance_dict:
# Create a case-insensitive map of importance keys
importance_map_lower = {k.lower(): v for k, v in importance_dict.items()}
# Map f0, f1, f2... to actual feature names
# OR use the feature names directly if they exist in the dict (case-insensitive)
for i, fname in enumerate(feature_names):
f_key = f'f{i}'
fname_lower = fname.lower()
if fname in importance_dict:
importances[fname] = float(importance_dict[fname])
elif fname_lower in importance_map_lower:
importances[fname] = float(importance_map_lower[fname_lower])
elif f_key in importance_dict:
importances[fname] = float(importance_dict[f_key])
else:
importances[fname] = 0.0
# Debug print
print(f"Mapped {len(importances)} features. Top 3: {list(importances.items())[:3]}")
break
except Exception as e:
print(f"get_score({importance_type}) failed: {e}")
continue
# If still empty, try without importance_type
if not importances:
try:
importance_dict = model.get_score()
for i, fname in enumerate(feature_names):
key = f'f{i}'
if key in importance_dict:
importances[fname] = float(importance_dict[key])
else:
importances[fname] = 0.0
except:
pass
# Skip SHAP calculation - just use what we have or return zeros
# SHAP is too slow for real-time analysis
if not importances or all(v == 0 for v in importances.values()):
print("No importances found, returning uniform values")
# Return uniform importance as fallback (fast)
importances = {fname: 1.0 for fname in feature_names}
if importances:
return {
"success": True,
"importances_dict": importances,
"feature_count": len(importances)
}
else:
raise HTTPException(status_code=500, detail="Could not extract feature importance")
except Exception as e:
import traceback
print(traceback.format_exc())
raise HTTPException(status_code=500, detail=f"Error calculating importance: {str(e)}")
else:
raise HTTPException(status_code=503, detail="Model not loaded")
except pd.errors.EmptyDataError:
raise HTTPException(status_code=400, detail="CSV file is empty")
except pd.errors.ParserError:
raise HTTPException(status_code=400, detail="Invalid CSV format")
except Exception as e:
import traceback
print(traceback.format_exc())
raise HTTPException(status_code=500, detail=f"Error processing file: {str(e)}")
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