backend-junPreP Update(Update whitelist model), front-urlpredictor.jsx UI Update

backend/app/exe.py

@@ -9,7 +9,7 @@ import os
 
 # 모델 및 스케일러 경로 (FastAPI 기준으로 맞춰서 절대 경로 또는 경로 설정)
 BASE_DIR = os.path.dirname(os.path.abspath(__file__))
-MODEL_PATH = os.path.join(BASE_DIR, "models", "Recall_0.77.keras")
+MODEL_PATH = os.path.join(BASE_DIR, "models", "White_list_model.keras")
 SCALER_PATH = os.path.join(BASE_DIR, "models", "scaler.pkl")
 
 # 모델 및 스케일러 로드 (1회만 수행)
@@ -36,16 +36,17 @@ def predict_url_maliciousness(url: str) -> dict:
 
     # 예측
     prediction = predict_with_model(model, input_scaled)
-    malicious_prob = float(prediction[0][0])
+    malicious_prob = float(prediction[0][0].numpy())
+
 
     # 임계값 기반 판단
     is_malicious = bool(malicious_prob > BEST_THRESHOLD)
 
-    # Ensure all values are Python native types (not numpy types)
+# 예: malicious_probability가 np.float32 타입일 경우
     return {
         "url": str(url),
-        "malicious_probability": float(malicious_prob),
+        "malicious_probability": malicious_prob,   
-        "is_malicious": bool(is_malicious),
+        "is_malicious": is_malicious,               
         "threshold": float(BEST_THRESHOLD)          
     }
 

backend/app/junPreP.py

@@ -2,11 +2,44 @@ import re
 from urllib.parse import urlparse, parse_qs
 import tldextract
 import zlib
-import re
-from urllib.parse import urlparse
 from collections import Counter
 import math
 
+def url_is_whitelisted(url):
+    trusted_domains = [
+    # 1. 포털 / 검색엔진
+    'naver.com', 'daum.net', 'google.com', 'bing.com', 'yahoo.com',
+
+    # 2. 소셜 미디어 / 커뮤니케이션
+    'facebook.com', 'instagram.com', 'twitter.com', 'x.com', 'linkedin.com',
+    'whatsapp.com', 'kakao.com', 'kakaocorp.com',
+
+    # 3. 동영상 / 스트리밍
+    'youtube.com', 'netflix.com', 'twitch.tv', 'tving.com', 'watcha.com',
+
+    # 4. 쇼핑 / 이커머스
+    'amazon.com', 'gmarket.co.kr', '11st.co.kr', 'coupang.com', 'ssg.com', 'wemakeprice.com',
+
+    # 5. 금융 / 결제
+    'paypal.com', 'kbfg.com', 'shinhan.com', 'hanafn.com', 'wooribank.com', 
+    'kakaobank.com', 'toss.im',
+
+    # 6. 공공기관 / 교육
+    'gov.kr', 'moe.go.kr', 'epeople.go.kr', 'pusan.ac.kr', 'ac.kr', 
+
+    # 7. IT / 기술
+    'apple.com', 'microsoft.com', 'adobe.com', 'github.com', 'stackoverflow.com'
+]
+    
+    try:
+        domain = urlparse(url if '//' in url else '//' + url).netloc.lower()
+        for trusted in trusted_domains:
+            if domain.endswith(trusted):
+                return True
+        return False
+    except:
+        return False
+
 
 
 def check_similar_brand(url):
@@ -159,7 +192,44 @@ def extract_features(url):
     else:
         url_length_cat = 3
         
+    if url_is_whitelisted(url):
+        return {
+            # 화이트리스트 URL이면 특징값들을 "정상적"으로 처리되도록 설정
+            "url_length_cat": 1,
+            "num_dots": 1,
+            "num_digits": 0,
+            "num_special_chars": 1,
+            "url_keyword": 0,
+            "num_underbar": 0,
+            "extract_consecutive_numbers": 0,
+            "number": 0,
+            "upper": 0,
+
+            "is_common_tld": 1,
+            "is country_tld": 0,
+            "is_suspicious_tld": 0,
+
+            "domain_length": 5,
+            "has_subdomain": 0,
+            "subdomain_length": 0,
+            "subdomain_count": 0,
+
+            "path_depth": 0,
+            "has_query": 0,
+            "query_length": 0,
+            "query_param_count": 0,
+            "url_shorteners": 0,
+
+            "compression_ratio": 1.0,
+            "check_similar_brand": 0,
+            "entropy": 3.0,
+            "digit_ratio": 0.0,
+            "special_char_ratio": 0.1
+        }
+    
+
     return {
+        
         # "url_length": url_length,
         "url_length_cat": url_length_cat,
         "num_dots": url.count("."),

backend/app/main.py

@@ -2,7 +2,6 @@ from fastapi import FastAPI
 from pydantic import BaseModel
 from app.model_load import use_model  # predictor.py에서 함수 import
 from app.exe import predict_url_maliciousness
-from app.utils import convert_numpy_to_python_types
 from fastapi.middleware.cors import CORSMiddleware
 
 app = FastAPI()
@@ -28,13 +27,15 @@ def root():
 def predict(request: UrlRequest):
      url = request.url
 
-     result_model1 = convert_numpy_to_python_types(use_model(url))
+     result_model1 = use_model(url)
-     result_model2 = convert_numpy_to_python_types(predict_url_maliciousness(url))
+     result_model2 = predict_url_maliciousness(url)
+    #  print("model1 : ")
+    #  print(result_model1.values())
+    #  print("model2 : ")
+    #  print(result_model2.values())
 
-     response_data = {
+     return {
-         "url": url,
+         "url" : url,
-         "model1": result_model1,
+        "model1": result_model1,
-         "model2": result_model2
+        "model2": result_model2
-     }
+    }
-     
-     return convert_numpy_to_python_types(response_data)

backend/app/model_load.py

@@ -29,6 +29,12 @@ def use_model(url : str):
     input_data = featured_df[features_cols]
 
     # 학습된 모델에 적용
-    model_pred = round(float(np.mean([model.predict_proba(input_data)[:, 1] for model in models_load])), 4)
+    model_pred = round(np.mean([model.predict_proba(input_data)[:, 1] for model in models_load]), 4)
 
-    return model_pred
+    #return model_pred
+    return {
+        "url" : url,
+        "malicious_probability" : float(model_pred),
+        "is_malicious" : bool(model_pred > best_threshold),
+        "threshold" : float(best_threshold)
+    }

backend/app/predictor.py

@@ -44,7 +44,7 @@ def predict_url(url: str) -> dict:
         input_data = preprocessed[features_cols]
         
         # ✅ 전처리된 데이터 확인
-        print("Preprocessed input:", input_data)
+        #print("Preprocessed input:", input_data)
 
         # 평균 확률 계산
         probs = [float(model.predict_proba(input_data)[0, 1]) for model in models_load]
@@ -61,8 +61,8 @@ def predict_url(url: str) -> dict:
         # 예: malicious_probability가 np.float32 타입일 경우
         return {
             "url": url,
-            "malicious_probability": mean_pred,  # ⬅️ numpy -> float
+            "malicious_probability": mean_pred,  
-            "is_malicious": bool(is_malicious),         # ⬅️ numpy -> bool
+            "is_malicious": is_malicious,         
             "threshold": float(BEST_THRESHOLD)          # ⬅️ numpy -> float
         }
 

backend/app/testexe.py

@@ -0,0 +1,4 @@
+from exe import predict_url_maliciousness
+
+result_model2 = predict_url_maliciousness("www.naver.com")
+print(result_model2)

backend/app/utils.py

@@ -1,18 +0,0 @@
-import numpy as np
-
-def convert_numpy_to_python_types(obj):
-    """
-    Recursively convert numpy types to native Python types.
-    """
-    if isinstance(obj, np.ndarray):
-        return convert_numpy_to_python_types(obj.tolist())
-    elif isinstance(obj, np.number):
-        return float(obj) if isinstance(obj, np.floating) else int(obj)
-    elif isinstance(obj, np.bool_):
-        return bool(obj)
-    elif isinstance(obj, dict):
-        return {k: convert_numpy_to_python_types(v) for k, v in obj.items()}
-    elif isinstance(obj, list) or isinstance(obj, tuple):
-        return [convert_numpy_to_python_types(item) for item in obj]
-    else:
-        return obj

react-url-checker/package.json

@@ -12,10 +12,11 @@
     "@types/react": "^19.1.0",
     "@types/react-dom": "^19.1.1",
     "axios": "^1.8.4",
+    "framer-motion": "^12.9.2",
     "react": "^19.1.0",
     "react-dom": "^19.1.0",
     "react-icons": "^5.5.0",
-    "react-scripts": "^3.0.1",
+    "react-scripts": "^5.0.1",
     "web-vitals": "^2.1.4"
   },
   "scripts": {
@@ -43,9 +44,11 @@
     ]
   },
   "devDependencies": {
+    "@babel/preset-react": "^7.26.3",
     "autoprefixer": "^10.4.21",
+    "eslint": "^8.57.1",
     "postcss": "^8.5.3",
     "tailwindcss": "^3.3.5",
-    "typescript": "^5.3.3"
+    "typescript": "^4.1.2"
   }
 }

react-url-checker/public/index.html

@@ -3,7 +3,7 @@
   <head>
     <meta charset="UTF-8" />
     <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-    <title>URL 악성 판별기</title>
+    <title></title>
   </head>
   <body>
     <div id="root"></div>

react-url-checker/src/App.js

@@ -5,9 +5,8 @@ import './App.css';
 
 function App() {
   return (
-<div className="min-h-screen bg-gray-100 flex flex-col justify-center">
+<div className="min-h-screen bg-sky-200 flex flex-col justify-center">
   <div className="container mx-auto px-4 text-center">
-    <h1 className="text-3xl font-bold text-blue-600 mb-6">🔍 악성 URL 판별기</h1>
     <UrlPredictor />
   </div>
 </div>

react-url-checker/src/components/UrlPredictor.jsx

@@ -1,5 +1,7 @@
 import React, { useState } from "react";
 import axios from "axios";
+import { motion } from "framer-motion"; // 애니메이션용
+import { FaSearch, FaRedo } from "react-icons/fa"; // 아이콘용
 
 const UrlPredictor = () => {
   const [url, setUrl] = useState("");
@@ -26,79 +28,88 @@ const UrlPredictor = () => {
     }
   };
 
-  // 모델 정보 정의 (title + 키)
   const models = [
-    { key: "old_model", title: "🧠 기존 모델 (Ho)" },
+    { key: "model1", title: "HO 모델" },
-    { key: "new_model", title: "🚀 개선 모델 (Jun)" },
+    { key: "model2", title: "Jun 모델" },
   ];
 
   return (
-    <div className="min-h-screen bg-gray-100 p-6">
+    <div className="min-h-screen bg-blue-50 p-8">
-      {!results ? (
+      <div className="grid grid-cols-1 md:grid-cols-2 gap-8 h-full">
-        <div className="flex justify-center items-center h-full">
+
-          <form onSubmit={handleSubmit} className="flex gap-4 w-full max-w-2xl">
+        {/* 왼쪽 입력창 */}
+        <div className="flex flex-col justify-center items-center gap-6">
+          <h1 className="text-2x1 font-bold text-blue-700">URL 판별기</h1>
+          <form onSubmit={handleSubmit} className="flex gap-2 w-full max-w-md">
             <input
               type="text"
               value={url}
               onChange={(e) => setUrl(e.target.value)}
               placeholder="URL을 입력하세요"
-              className="flex-grow px-4 py-2 border border-gray-300 rounded shadow"
+              className="flex-grow px-4 py-2 border border-gray-300 rounded-lg shadow-md focus:outline-none focus:ring-2 focus:ring-blue-400"
               required
             />
             <button
               type="submit"
-              className="bg-blue-600 text-white px-6 py-2 rounded shadow hover:bg-blue-700 transition"
+              className="bg-blue-600 text-white px-6 py-2 flex items-center gap-2 rounded-lg shadow-md hover:bg-blue-700 transition"
             >
-              ✅ 검사하기
+              <FaSearch /> 검사
             </button>
           </form>
+          {loading && (
+            <div className="flex items-center gap-2">
+              <div className="w-6 h-6 border-4 border-blue-400 border-t-transparent rounded-full animate-spin"></div>
+              <p className="text-blue-600 font-semibold">분석 중...</p>
+            </div>
+          )}
+          {error && <p className="text-red-500">❌ {error}</p>}
         </div>
-      ) : (
-        <div className="grid grid-cols-2 gap-6">
-          {/* 좌측 입력창 */}
-          <div className="flex flex-col gap-4">
-            <form onSubmit={handleSubmit} className="flex gap-2">
-              <input
-                type="text"
-                value={url}
-                onChange={(e) => setUrl(e.target.value)}
-                className="flex-grow px-4 py-2 border border-gray-300 rounded shadow"
-                placeholder="URL을 다시 입력해보세요"
-                required
-              />
-              <button
-                type="submit"
-                className="bg-blue-600 text-white px-4 py-2 rounded hover:bg-blue-700 transition"
-              >
-                다시 검사
-              </button>
-            </form>
-            {loading && <p>🔍 분석 중...</p>}
-            {error && <p className="text-red-500">❌ {error}</p>}
-          </div>
 
-          {/* 우측 결과 반복 렌더링 */}
+        {/* 오른쪽 결과창 */}
-          <div className="flex flex-col gap-4">
+        <div className="flex flex-col gap-6">
-            {models.map((model) => {
+          {results ? (
+            models.map((model) => {
               const data = results[model.key];
+              if (!data) return null;
+
               return (
-                <div key={model.key} className="bg-white rounded p-4 shadow">
+                <motion.div
-                  <h2 className="text-lg font-bold mb-2">{model.title}</h2>
+                  key={model.key}
-                  <p>
+                  initial={{ opacity: 0, y: 30 }}
-                    악성 확률: <strong>{(data.prob * 100).toFixed(2)}%</strong>
+                  animate={{ opacity: 1, y: 0 }}
+                  transition={{ duration: 0.6 }}
+                  className="bg-white rounded-2xl p-6 shadow-lg border border-gray-200"
+                >
+                  <h2 className="text-xl font-bold mb-4 text-gray-800">{model.title}</h2>
+                  <p className="mb-2 text-gray-700">
+                    악성 확률:{" "}
+                    <strong>
+                      {(data.malicious_probability * 100).toFixed(2)}%
+                    </strong>
                   </p>
                   <p>
                     판별 결과:{" "}
-                    <strong className={data.malicious ? "text-red-600" : "text-green-600"}>
+                    <strong
-                      {data.malicious ? "⚠️ 악성 URL" : "✅ 정상 URL"}
+                      className={
+                        data.is_malicious
+                          ? "text-red-600"
+                          : "text-green-600"
+                      }
+                    >
+                      {data.is_malicious ? "⚠️ 악성 URL" : "✅ 정상 URL"}
                     </strong>
                   </p>
-                </div>
+                </motion.div>
               );
-            })}
+            })
-          </div>
+          ) : (
+            <div className="text-gray-500 flex items-center justify-center h-full">
+              결과가 여기에 표시됩니다.
+            </div>
+          )}
         </div>
-      )}
+
+      </div>
     </div>
   );
 };

react-url-checker/tsconfig (copy 1).json

@@ -0,0 +1,26 @@
+{
+  "compilerOptions": {
+    "target": "es5",
+    "lib": [
+      "dom",
+      "dom.iterable",
+      "esnext"
+    ],
+    "allowJs": true,
+    "skipLibCheck": true,
+    "esModuleInterop": true,
+    "allowSyntheticDefaultImports": true,
+    "strict": true,
+    "forceConsistentCasingInFileNames": true,
+    "noFallthroughCasesInSwitch": true,
+    "module": "esnext",
+    "moduleResolution": "node",
+    "resolveJsonModule": true,
+    "isolatedModules": true,
+    "noEmit": true,
+    "jsx": "preserve"
+  },
+  "include": [
+    "src"
+  ]
+}