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Nam/Feature.py

@@ -0,0 +1,38 @@
+import re
+from collections import Counter
+from scipy.stats import entropy
+
+def calculate_url_entropy(url):
+    counter = Counter(url)
+    probabilities = [count / len(url) for count in counter.values()]
+    return entropy(probabilities, base=2)
+
+def extract_url_features(url):
+    suspicious_words = [
+        'login', 'verify', 'update', 'confirm',
+        'account', 'secure', 'ebayisapi', 'banking'
+    ]
+
+    return {
+        'digit_count': len(re.findall(r'\d', url)),
+        'dash_count': url.count('-'),
+        'underscore_count': url.count('_'),
+        'percent_count': url.count('%'),
+        'equal_count': url.count('='),
+        'question_count': url.count('?'),
+        'at_count': url.count('@'),
+        'count_of_exclamation': url.count('!'),
+        'count_of_dot': url.count('.'),
+        'count_of_double_slash': url.count('//'),
+        'special_char_count': len(re.findall(r'[^a-zA-Z0-9]', url)),
+        'is_ip_in_url': bool(re.search(r'\b(?:\d{1,3}\.){3}\d{1,3}\b', url)),
+        'has_www': 'www' in url,
+        'suspicious_word_count': sum(word in url.lower() for word in suspicious_words),
+        'path_depth': url.count('/') - 2,
+        'has_long_digit_sequence': bool(re.search(r'\d{4,}', url)),
+        'has_multiple_dash': bool(re.search(r'-{2,}', url)),
+        'has_https': url.startswith('https'),
+        'ends_with_common_extension': url.endswith(('.html', '.php')),
+        'url_length': len(url),  # ✅ 추가
+        'url_entropy': calculate_url_entropy(url)  # ✅ 추가
+    }

Nam/model.running_code.py

@@ -0,0 +1,54 @@
+import pandas as pd
+import pickle
+from tensorflow.keras.models import load_model
+from Feature import extract_url_features
+from collections import Counter
+from scipy.stats import entropy
+import tensorflow as tf
+
+# 🔹 URL 엔트로피 계산 함수
+def calculate_url_entropy(url):
+    counter = Counter(url)
+    probabilities = [count / len(url) for count in counter.values()]
+    return entropy(probabilities, base=2)
+
+# 🔹 스케일러 불러오기
+with open("scaler.pkl", "rb") as f:
+    scaler = pickle.load(f)
+
+# 🔹 모델 불러오기
+model = load_model("best_model.h5")
+
+# 🔹 예측 함수
+@tf.function(reduce_retracing=True)
+def predict_with_model(model, input_data):
+    return model(input_data)
+
+# 🔹 입력 URL 받기
+url = input("URL입력 : ")
+
+# 🔹 Feature.py에서 피처 추출
+features = extract_url_features(url)
+
+# 🔹 누락된 피처 보완
+features['url_length'] = len(url)
+features['url_entropy'] = calculate_url_entropy(url)
+
+# 🔹 데이터프레임 생성 및 정렬
+input_df = pd.DataFrame([features])
+expected_columns = list(scaler.feature_names_in_)
+input_df = input_df[expected_columns]
+
+# 🔹 스케일링
+input_scaled = scaler.transform(input_df)
+
+# 🔹 예측
+prediction = predict_with_model(model, input_scaled)
+score = float(prediction.numpy()[0][0])  # 🔥 정확히 float으로 변환
+
+# 🔹 출력
+threshold = 0.5
+if score > threshold:
+    print(f"악성 (악성일 확률: {score:.4f})")
+else:
+    print(f"정상 (정상일 확률: {1 - score:.4f})")

backend/app/exe.py

@@ -1,52 +1,53 @@
-from app.junPreP import extract_features
-import numpy as np
-import pickle
-import pandas as pd
-from sklearn.preprocessing import MinMaxScaler
-from tensorflow.keras.models import load_model
-import tensorflow as tf
-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")
-SCALER_PATH = os.path.join(BASE_DIR, "models", "scaler.pkl")
-
-# 모델 및 스케일러 로드 (1회만 수행)
-model = load_model(MODEL_PATH)
-with open(SCALER_PATH, 'rb') as f:
-    scaler = pickle.load(f)
-
-# @tf.function으로 추론 최적화
-@tf.function(reduce_retracing=True)
-def predict_with_model(model, input_data):
-    return model(input_data)
-
-# Threshold (적절히 조정 가능)
-BEST_THRESHOLD = 0.4034
-
-# 📦 예측 함수 정의 (FastAPI에서 import해서 사용)
-def predict_url_maliciousness(url: str) -> dict:
-    # 특성 추출
-    features = extract_features(url)
-    input_df = pd.DataFrame([list(features.values())], columns=features.keys())
-
-    # 스케일링
-    input_scaled = scaler.transform(input_df)
-
-    # 예측
-    prediction = predict_with_model(model, input_scaled)
-    malicious_prob = float(prediction[0][0])
-
-    # 임계값 기반 판단
-    is_malicious = bool(malicious_prob > BEST_THRESHOLD)
-
-    # Ensure all values are Python native types (not numpy types)
-    return {
-        "url": str(url),
-        "malicious_probability": float(malicious_prob),
-        "is_malicious": bool(is_malicious),
-        "threshold": float(BEST_THRESHOLD)
-    }
-
-
+from app.junPreP import extract_features
+import numpy as np
+import pickle
+import pandas as pd
+from sklearn.preprocessing import MinMaxScaler
+from tensorflow.keras.models import load_model
+import tensorflow as tf
+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", "White_list_model.keras")
+SCALER_PATH = os.path.join(BASE_DIR, "models", "scaler.pkl")
+
+# 모델 및 스케일러 로드 (1회만 수행)
+model = load_model(MODEL_PATH)
+with open(SCALER_PATH, 'rb') as f:
+    scaler = pickle.load(f)
+
+# @tf.function으로 추론 최적화
+@tf.function(reduce_retracing=True)
+def predict_with_model(model, input_data):
+    return model(input_data)
+
+# Threshold (적절히 조정 가능)
+BEST_THRESHOLD = 0.4034
+
+# 📦 예측 함수 정의 (FastAPI에서 import해서 사용)
+def predict_url_maliciousness(url: str) -> dict:
+    # 특성 추출
+    features = extract_features(url)
+    input_df = pd.DataFrame([list(features.values())], columns=features.keys())
+
+    # 스케일링
+    input_scaled = scaler.transform(input_df)
+
+    # 예측
+    prediction = predict_with_model(model, input_scaled)
+    malicious_prob = float(prediction[0][0].numpy())
+
+
+    # 임계값 기반 판단
+    is_malicious = bool(malicious_prob > BEST_THRESHOLD)
+
+# 예: malicious_probability가 np.float32 타입일 경우
+    return {
+        "url": str(url),
+        "malicious_probability": malicious_prob,   
+        "is_malicious": is_malicious,               
+        "threshold": float(BEST_THRESHOLD)          
+    }
+
+

backend/app/junPreP.py

@@ -1,204 +1,274 @@
-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 check_similar_brand(url):
-    # 자주 사용되는 브랜드/도메인 목록
-    common_brands = {
-        'google', 'facebook', 'amazon', 'microsoft', 'apple', 
-        'netflix', 'paypal', 'twitter', 'instagram', 'linkedin',
-        'youtube', 'yahoo', 'gmail', 'whatsapp', 'tiktok',
-        'geocities', 'angelfire', 'newadvent', 'wikipedia',
-    }
-    
-    # 2. 유사 브랜드 확인
-    try:
-        # URL 파싱
-        parsed = urlparse(url if '//' in url else '//' + url)
-        domain = parsed.netloc.lower() if parsed.netloc else url.lower()
-        
-        for brand in common_brands:
-            if brand not in domain:
-                similar = False
-                # 비슷한 철자 패턴 확인
-                patterns = [
-                    brand.replace('o', '0'),
-                    brand.replace('i', '1'),
-                    brand.replace('l', '1'),
-                    brand.replace('e', '3'),
-                    brand.replace('a', '4'),
-                    brand.replace('s', '5'),
-                    brand + '-',
-                    brand + '_',
-                    brand[:-1],  # 마지막 문자 제거
-                    ''.join(c + c for c in brand),  # 문자 중복
-                ]
-                
-                for pattern in patterns:
-                    if pattern in domain:
-                        similar = True
-                        break
-                
-                if similar:
-                    return True  # 유사 브랜드가 발견되면 True 반환
-        
-    except Exception as e:
-        return False  # 예외 발생 시 False 반환
-    
-    return False  # 유사 브랜드가 없으면 False 반환
-
-
-
-# url 압축 비율 계산 함수
-def compression_ratio(url: str) -> float:
-    if not url:
-        return 0.0
-    original_length = len(url.encode('utf-8'))
-    compressed_data = zlib.compress(url.encode('utf-8'))
-    compressed_length = len(compressed_data)
-    return compressed_length / original_length
-
-
-def extract_features(url):
-    parsed_url = urlparse(url)
-    suspicious_keywords = [
-        'login', 'verify', 'account', 'update', 'secure', 'banking', 
-        'paypal', 'confirm', 'signin', 'auth', 'redirect', 'free', 
-        'bonus', 'admin', 'support', 'server', 'password', 'click', 
-        'urgent', 'immediate', 'alert', 'security', 'prompt'
-    ]
-    
-    additional_keywords = [
-        'verify', 'wallet', 'cryptocurrency', 'bitcoin', 'ethereum',
-        'validation', 'authenticate', 'reset', 'recover', 'access',
-        'limited', 'offer', 'prize', 'win', 'winner', 'payment',
-        'bank', 'credit', 'debit', 'card', 'expire', 'suspension',
-        'unusual', 'activity', 'verify', 'document', 'invoice'
-    ]
-    
-    all_keywords = list(set(suspicious_keywords + additional_keywords))
-
-    contains_keyword = 0
-    keyword_count = 0
-    for keyword in all_keywords:
-        if re.search(r'\b' + keyword + r'\b', url, re.IGNORECASE):
-            contains_keyword = 1
-            keyword_count += 1
-    
-    url_length = len(url)
-    extracted = tldextract.extract(url)
-    tld = extracted.suffix
-    domain = extracted.domain
-    subdomain = extracted.subdomain
-
-    tld_length = len(tld) if tld else 0
-    common_tlds = ['com', 'org', 'net', 'edu', 'gov', 'mil', 'io', 'co', 'info', 'biz']
-    is_common_tld = 1 if tld in common_tlds else 0
-    country_tlds = ['us', 'uk', 'ca', 'au', 'de', 'fr', 'jp', 'cn', 'ru', 'br', 'in', 'it', 'es']
-    is_country_tld = 1 if tld in country_tlds else 0
-    suspicious_tlds = ['xyz', 'top', 'club', 'online', 'site', 'icu', 'vip', 'work', 'rest', 'fit']
-    is_suspicious_tld = 1 if tld in suspicious_tlds else 0
-    url_shorteners = ['bit.ly', 'tinyurl.com', 'goo.gl', 't.co', 'ow.ly', 'is.gd', 'buff.ly', 'adf.ly', 'tiny.cc']
-    full_domain = f"{domain}.{tld}" if tld else domain
-    is_shortened = 1 if full_domain in url_shorteners else 0
-
-
-    domain_length = len(domain) if domain else 0
-    has_subdomain = 1 if subdomain else 0
-    subdomain_length = len(subdomain) if subdomain else 0
-    subdomain_count = len(subdomain.split('.')) if subdomain else 0 
-
-    path = parsed_url.path
-    path_length = len(path)
-    path_depth = path.count('/') if path else 0
-
-    query = parsed_url.query
-    has_query = 1 if query else 0
-    query_length = len(query) if query else 0
-    query_params = parse_qs(query)
-    query_param_count = len(query_params) if query_params else 0
-
-    has_fragment = 1 if parsed_url.fragment else 0
-    fragment_length = len(parsed_url.fragment) if parsed_url.fragment else 0
-    
-    # Character type ratios
-    letter_count = sum(c.isalpha() for c in url)
-    digit_count = sum(c.isdigit() for c in url)
-    special_char_count = len(re.findall(r'[^a-zA-Z0-9]', url))
-    
-    letter_ratio = letter_count / url_length if url_length > 0 else 0
-    digit_ratio = digit_count / url_length if url_length > 0 else 0
-    special_char_ratio = special_char_count / url_length if url_length > 0 else 0
-    
-    # Character distribution and entropy
-    if url:
-        char_counts = Counter(url)
-        total_chars = len(url)
-        char_frequencies = {char: count/total_chars for char, count in char_counts.items()}
-        entropy = -sum(freq * math.log2(freq) for freq in char_frequencies.values())
-    else:
-        entropy = 0
-
-
-
-
-
-    if url_length <= 13:
-        url_length_cat = 0  
-    elif url_length <= 18:
-        url_length_cat = 1 
-    elif url_length <= 25:
-        url_length_cat = 2 
-    else:
-        url_length_cat = 3 
-
-    return {
-        # "url_length": url_length,
-        "url_length_cat": url_length_cat,
-        "num_dots": url.count("."),
-        "num_digits": sum(c.isdigit() for c in url),
-        "num_special_chars": len(re.findall(r"[^a-zA-Z0-9]", url)),
-        "url_keyword": contains_keyword,
-        # "url_keyword_count": keyword_count,
-        "num_underbar": url.count("_"),
-        "extract_consecutive_numbers": int(bool(re.findall(r'(\d)\1+', url))),
-        "number": int(bool(len(re.findall(r'(\d)(?!\1)(\d)(?!\2)(\d)', url)))),
-        "upper": int(any(c.isupper() for c in url)),
-
-        "is_common_tld": is_common_tld,
-        "is country_tld": is_country_tld,
-        "is_suspicious_tld": is_suspicious_tld,
-
-        "domain_length": domain_length,
-        "has_subdomain": has_subdomain,
-        "subdomain_length": subdomain_length,
-        "subdomain_count": subdomain_count,
-
-        # "path_length": path_length,
-        "path_depth": path_depth,
-        "has_query": has_query,
-        "query_length": query_length,
-        "query_param_count": query_param_count,
-        # "has_fragment": has_fragment,
-        # "fragment_length": fragment_length,
-        "url_shorteners": is_shortened,
-
-        # 새로 추가된 특성
-        "compression_ratio": compression_ratio(url),
-        "check_similar_brand" : check_similar_brand(url),
- 
-        # Advanced text analysis
-        "entropy": entropy,
-        #"letter_ratio": letter_ratio,
-        "digit_ratio": digit_ratio,
-        "special_char_ratio": special_char_ratio
-
-        
-    }
+import re
+from urllib.parse import urlparse, parse_qs
+import tldextract
+import zlib
+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):
+    # 자주 사용되는 브랜드/도메인 목록
+    common_brands = {
+        'google', 'facebook', 'amazon', 'microsoft', 'apple', 
+        'netflix', 'paypal', 'twitter', 'instagram', 'linkedin',
+        'youtube', 'yahoo', 'gmail', 'whatsapp', 'tiktok',
+        'geocities', 'angelfire', 'newadvent', 'wikipedia',
+    }
+    
+    # 2. 유사 브랜드 확인
+    try:
+        # URL 파싱
+        parsed = urlparse(url if '//' in url else '//' + url)
+        domain = parsed.netloc.lower() if parsed.netloc else url.lower()
+        
+        for brand in common_brands:
+            if brand not in domain:
+                similar = False
+                # 비슷한 철자 패턴 확인
+                patterns = [
+                    brand.replace('o', '0'),
+                    brand.replace('i', '1'),
+                    brand.replace('l', '1'),
+                    brand.replace('e', '3'),
+                    brand.replace('a', '4'),
+                    brand.replace('s', '5'),
+                    brand + '-',
+                    brand + '_',
+                    brand[:-1],  # 마지막 문자 제거
+                    ''.join(c + c for c in brand),  # 문자 중복
+                ]
+                
+                for pattern in patterns:
+                    if pattern in domain:
+                        similar = True
+                        break
+                
+                if similar:
+                    return True  # 유사 브랜드가 발견되면 True 반환
+        
+    except Exception as e:
+        return False  # 예외 발생 시 False 반환
+    
+    return False  # 유사 브랜드가 없으면 False 반환
+
+
+
+# url 압축 비율 계산 함수
+def compression_ratio(url: str) -> float:
+    if not url:
+        return 0.0
+    original_length = len(url.encode('utf-8'))
+    compressed_data = zlib.compress(url.encode('utf-8'))
+    compressed_length = len(compressed_data)
+    return compressed_length / original_length
+
+
+def extract_features(url):
+    parsed_url = urlparse(url)
+    suspicious_keywords = [
+        'login', 'verify', 'account', 'update', 'secure', 'banking', 
+        'paypal', 'confirm', 'signin', 'auth', 'redirect', 'free', 
+        'bonus', 'admin', 'support', 'server', 'password', 'click', 
+        'urgent', 'immediate', 'alert', 'security', 'prompt'
+    ]
+    
+    additional_keywords = [
+        'verify', 'wallet', 'cryptocurrency', 'bitcoin', 'ethereum',
+        'validation', 'authenticate', 'reset', 'recover', 'access',
+        'limited', 'offer', 'prize', 'win', 'winner', 'payment',
+        'bank', 'credit', 'debit', 'card', 'expire', 'suspension',
+        'unusual', 'activity', 'verify', 'document', 'invoice'
+    ]
+    
+    all_keywords = list(set(suspicious_keywords + additional_keywords))
+
+    contains_keyword = 0
+    keyword_count = 0
+    for keyword in all_keywords:
+        if re.search(r'\b' + keyword + r'\b', url, re.IGNORECASE):
+            contains_keyword = 1
+            keyword_count += 1
+    
+    url_length = len(url)
+    extracted = tldextract.extract(url)
+    tld = extracted.suffix
+    domain = extracted.domain
+    subdomain = extracted.subdomain
+
+    tld_length = len(tld) if tld else 0
+    common_tlds = ['com', 'org', 'net', 'edu', 'gov', 'mil', 'io', 'co', 'info', 'biz']
+    is_common_tld = 1 if tld in common_tlds else 0
+    country_tlds = ['us', 'uk', 'ca', 'au', 'de', 'fr', 'jp', 'cn', 'ru', 'br', 'in', 'it', 'es']
+    is_country_tld = 1 if tld in country_tlds else 0
+    suspicious_tlds = ['xyz', 'top', 'club', 'online', 'site', 'icu', 'vip', 'work', 'rest', 'fit']
+    is_suspicious_tld = 1 if tld in suspicious_tlds else 0
+    url_shorteners = ['bit.ly', 'tinyurl.com', 'goo.gl', 't.co', 'ow.ly', 'is.gd', 'buff.ly', 'adf.ly', 'tiny.cc']
+    full_domain = f"{domain}.{tld}" if tld else domain
+    is_shortened = 1 if full_domain in url_shorteners else 0
+
+
+    domain_length = len(domain) if domain else 0
+    has_subdomain = 1 if subdomain else 0
+    subdomain_length = len(subdomain) if subdomain else 0
+    subdomain_count = len(subdomain.split('.')) if subdomain else 0 
+
+    path = parsed_url.path
+    path_length = len(path)
+    path_depth = path.count('/') if path else 0
+
+    query = parsed_url.query
+    has_query = 1 if query else 0
+    query_length = len(query) if query else 0
+    query_params = parse_qs(query)
+    query_param_count = len(query_params) if query_params else 0
+
+    has_fragment = 1 if parsed_url.fragment else 0
+    fragment_length = len(parsed_url.fragment) if parsed_url.fragment else 0
+    
+    # Character type ratios
+    letter_count = sum(c.isalpha() for c in url)
+    digit_count = sum(c.isdigit() for c in url)
+    special_char_count = len(re.findall(r'[^a-zA-Z0-9]', url))
+    
+    letter_ratio = letter_count / url_length if url_length > 0 else 0
+    digit_ratio = digit_count / url_length if url_length > 0 else 0
+    special_char_ratio = special_char_count / url_length if url_length > 0 else 0
+    
+    # Character distribution and entropy
+    if url:
+        char_counts = Counter(url)
+        total_chars = len(url)
+        char_frequencies = {char: count/total_chars for char, count in char_counts.items()}
+        entropy = -sum(freq * math.log2(freq) for freq in char_frequencies.values())
+    else:
+        entropy = 0
+
+
+
+
+
+    if url_length <= 13:
+        url_length_cat = 0  
+    elif url_length <= 18:
+        url_length_cat = 1 
+    elif url_length <= 25:
+        url_length_cat = 2 
+    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("."),
+        "num_digits": sum(c.isdigit() for c in url),
+        "num_special_chars": len(re.findall(r"[^a-zA-Z0-9]", url)),
+        "url_keyword": contains_keyword,
+        # "url_keyword_count": keyword_count,
+        "num_underbar": url.count("_"),
+        "extract_consecutive_numbers": int(bool(re.findall(r'(\d)\1+', url))),
+        "number": int(bool(len(re.findall(r'(\d)(?!\1)(\d)(?!\2)(\d)', url)))),
+        "upper": int(any(c.isupper() for c in url)),
+
+        "is_common_tld": is_common_tld,
+        "is country_tld": is_country_tld,
+        "is_suspicious_tld": is_suspicious_tld,
+
+        "domain_length": domain_length,
+        "has_subdomain": has_subdomain,
+        "subdomain_length": subdomain_length,
+        "subdomain_count": subdomain_count,
+
+        # "path_length": path_length,
+        "path_depth": path_depth,
+        "has_query": has_query,
+        "query_length": query_length,
+        "query_param_count": query_param_count,
+        # "has_fragment": has_fragment,
+        # "fragment_length": fragment_length,
+        "url_shorteners": is_shortened,
+
+        # 새로 추가된 특성
+        "compression_ratio": compression_ratio(url),
+        "check_similar_brand" : check_similar_brand(url),
+ 
+        # Advanced text analysis
+        "entropy": entropy,
+        #"letter_ratio": letter_ratio,
+        "digit_ratio": digit_ratio,
+        "special_char_ratio": special_char_ratio
+
+        
+    }

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_model2 = convert_numpy_to_python_types(predict_url_maliciousness(url))
-     
-     response_data = {
-         "url": url,
-         "model1": result_model1,
-         "model2": result_model2
-     }
-     
-     return convert_numpy_to_python_types(response_data)
+     result_model1 = use_model(url)
+     result_model2 = predict_url_maliciousness(url)
+    #  print("model1 : ")
+    #  print(result_model1.values())
+    #  print("model2 : ")
+    #  print(result_model2.values())
+
+     return {
+         "url" : url,
+        "model1": result_model1,
+        "model2": result_model2
+    }

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
-            "is_malicious": bool(is_malicious),         # ⬅️ numpy -> bool
+            "malicious_probability": mean_pred,  
+            "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

jun/code.ipynb

@@ -2392,703 +2392,56 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [
     {
-     "data": {
-      "application/vnd.microsoft.datawrangler.viewer.v0+json": {
-       "columns": [
-        {
-         "name": "index",
-         "rawType": "object",
-         "type": "string"
-        },
-        {
-         "name": "label",
-         "rawType": "float64",
-         "type": "float"
-        },
-        {
-         "name": "url_length_cat",
-         "rawType": "float64",
-         "type": "float"
-        },
-        {
-         "name": "num_dots",
-         "rawType": "float64",
-         "type": "float"
-        },
-        {
-         "name": "num_digits",
-         "rawType": "float64",
-         "type": "float"
-        },
-        {
-         "name": "num_special_chars",
-         "rawType": "float64",
-         "type": "float"
-        },
-        {
-         "name": "url_keyword",
-         "rawType": "float64",
-         "type": "float"
-        },
-        {
-         "name": "num_underbar",
-         "rawType": "float64",
-         "type": "float"
-        },
-        {
-         "name": "extract_consecutive_numbers",
-         "rawType": "float64",
-         "type": "float"
-        },
-        {
-         "name": "number",
-         "rawType": "float64",
-         "type": "float"
-        },
-        {
-         "name": "upper",
-         "rawType": "float64",
-         "type": "float"
-        },
-        {
-         "name": "is_common_tld",
-         "rawType": "float64",
-         "type": "float"
-        },
-        {
-         "name": "is_country_tld",
-         "rawType": "float64",
-         "type": "float"
-        },
-        {
-         "name": "is_suspicious_tld",
-         "rawType": "float64",
-         "type": "float"
-        },
-        {
-         "name": "domain_length",
-         "rawType": "float64",
-         "type": "float"
-        },
-        {
-         "name": "has_subdomain",
-         "rawType": "float64",
-         "type": "float"
-        },
-        {
-         "name": "subdomain_length",
-         "rawType": "float64",
-         "type": "float"
-        },
-        {
-         "name": "subdomain_count",
-         "rawType": "float64",
-         "type": "float"
-        },
-        {
-         "name": "path_depth",
-         "rawType": "float64",
-         "type": "float"
-        },
-        {
-         "name": "has_query",
-         "rawType": "float64",
-         "type": "float"
-        },
-        {
-         "name": "query_length",
-         "rawType": "float64",
-         "type": "float"
-        },
-        {
-         "name": "query_param_count",
-         "rawType": "float64",
-         "type": "float"
-        },
-        {
-         "name": "url_shorteners",
-         "rawType": "float64",
-         "type": "float"
-        },
-        {
-         "name": "compression_ratio",
-         "rawType": "float64",
-         "type": "float"
-        },
-        {
-         "name": "entropy",
-         "rawType": "float64",
-         "type": "float"
-        },
-        {
-         "name": "digit_ratio",
-         "rawType": "float64",
-         "type": "float"
-        },
-        {
-         "name": "special_char_ratio",
-         "rawType": "float64",
-         "type": "float"
-        }
-       ],
-       "conversionMethod": "pd.DataFrame",
-       "ref": "c79a077e-8e52-4e42-b88f-dc9698b0fa30",
-       "rows": [
-        [
-         "count",
-         "6995056.0",
-         "6995056.0",
-         "6995056.0",
-         "6995056.0",
-         "6995056.0",
-         "6995056.0",
-         "6995056.0",
-         "6995056.0",
-         "6995056.0",
-         "6995056.0",
-         "6995056.0",
-         "6995056.0",
-         "6995056.0",
-         "6995056.0",
-         "6995056.0",
-         "6995056.0",
-         "6995056.0",
-         "6995056.0",
-         "6995056.0",
-         "6995056.0",
-         "6995056.0",
-         "6995056.0",
-         "6995056.0",
-         "6995056.0",
-         "6995056.0",
-         "6995056.0"
-        ],
-        [
-         "mean",
-         "0.22371472079708868",
-         "1.4435534183000107",
-         "1.546944584861079",
-         "1.6343590387267808",
-         "2.6635716711917676",
-         "0.0370789025849114",
-         "0.045005501028154746",
-         "0.056463736673444787",
-         "0.08128040719044995",
-         "0.0357764112252997",
-         "0.6133649251700057",
-         "0.12739140329970197",
-         "0.022784949827420967",
-         "10.464007150192936",
-         "0.21130266862767075",
-         "2.43731000866898",
-         "0.2660177416735477",
-         "0.6056849294701858",
-         "0.027221368921135157",
-         "1.9155892390282507",
-         "0.04228915393958247",
-         "0.0018421582329004942",
-         "1.4552534994784176",
-         "3.5360434022769756",
-         "0.029042428345387533",
-         "0.1102289088601276"
-        ],
-        [
-         "std",
-         "0.41673309122602675",
-         "1.1161203432813147",
-         "1.010078604927829",
-         "9.827940363271033",
-         "7.1618457272654",
-         "0.18895518694176003",
-         "0.6023702991784359",
-         "0.23081505741717664",
-         "0.273265280035072",
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-         "0.4869788780260291",
-         "0.33341093196934307",
-         "0.14921728811320575",
-         "5.0652546813544035",
-         "0.4082326232468674",
-         "6.90096602515224",
-         "0.6272395647222854",
-         "1.6003209664806863",
-         "0.1627279010519657",
-         "19.702068343354906",
-         "0.35208851309719974",
-         "0.04288082262284407",
-         "0.24856536988340924",
-         "0.47898938276414027",
-         "0.08255957016074264",
-         "0.046338026902092454"
-        ],
-        [
-         "min",
-         "0.0",
-         "0.0",
-         "0.0",
-         "0.0",
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-         "0.0",
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-         "-0.0",
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-        ],
-        [
-         "25%",
-         "0.0",
-         "0.0",
-         "1.0",
-         "0.0",
-         "1.0",
-         "0.0",
-         "0.0",
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-         "7.0",
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-         "3.238901256602631",
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-        ],
-        [
-         "50%",
-         "0.0",
-         "1.0",
-         "1.0",
-         "0.0",
-         "2.0",
-         "0.0",
-         "0.0",
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-         "2.0",
-         "2.0",
-         "0.0",
-         "3.0",
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-         "0.0",
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-         "171.0",
-         "2011.0",
-         "8198.0",
-         "1.0",
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-         "1.0",
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-         "1.0",
-         "63.0",
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-         "237.0",
-         "38.0",
-         "136.0",
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-         "8367.0",
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-         "1.0",
-         "5.0",
-         "6.570554108088201",
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-         "1.0"
-        ]
-       ],
-       "shape": {
-        "columns": 26,
-        "rows": 8
-       }
-      },
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>label</th>\n",
-       "      <th>url_length_cat</th>\n",
-       "      <th>num_dots</th>\n",
-       "      <th>num_digits</th>\n",
-       "      <th>num_special_chars</th>\n",
-       "      <th>url_keyword</th>\n",
-       "      <th>num_underbar</th>\n",
-       "      <th>extract_consecutive_numbers</th>\n",
-       "      <th>number</th>\n",
-       "      <th>upper</th>\n",
-       "      <th>...</th>\n",
-       "      <th>subdomain_count</th>\n",
-       "      <th>path_depth</th>\n",
-       "      <th>has_query</th>\n",
-       "      <th>query_length</th>\n",
-       "      <th>query_param_count</th>\n",
-       "      <th>url_shorteners</th>\n",
-       "      <th>compression_ratio</th>\n",
-       "      <th>entropy</th>\n",
-       "      <th>digit_ratio</th>\n",
-       "      <th>special_char_ratio</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>count</th>\n",
-       "      <td>6.995056e+06</td>\n",
-       "      <td>6.995056e+06</td>\n",
-       "      <td>6.995056e+06</td>\n",
-       "      <td>6.995056e+06</td>\n",
-       "      <td>6.995056e+06</td>\n",
-       "      <td>6.995056e+06</td>\n",
-       "      <td>6.995056e+06</td>\n",
-       "      <td>6.995056e+06</td>\n",
-       "      <td>6.995056e+06</td>\n",
-       "      <td>6.995056e+06</td>\n",
-       "      <td>...</td>\n",
-       "      <td>6.995056e+06</td>\n",
-       "      <td>6.995056e+06</td>\n",
-       "      <td>6.995056e+06</td>\n",
-       "      <td>6.995056e+06</td>\n",
-       "      <td>6.995056e+06</td>\n",
-       "      <td>6.995056e+06</td>\n",
-       "      <td>6.995056e+06</td>\n",
-       "      <td>6.995056e+06</td>\n",
-       "      <td>6.995056e+06</td>\n",
-       "      <td>6.995056e+06</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>mean</th>\n",
-       "      <td>2.237147e-01</td>\n",
-       "      <td>1.443553e+00</td>\n",
-       "      <td>1.546945e+00</td>\n",
-       "      <td>1.634359e+00</td>\n",
-       "      <td>2.663572e+00</td>\n",
-       "      <td>3.707890e-02</td>\n",
-       "      <td>4.500550e-02</td>\n",
-       "      <td>5.646374e-02</td>\n",
-       "      <td>8.128041e-02</td>\n",
-       "      <td>3.577641e-02</td>\n",
-       "      <td>...</td>\n",
-       "      <td>2.660177e-01</td>\n",
-       "      <td>6.056849e-01</td>\n",
-       "      <td>2.722137e-02</td>\n",
-       "      <td>1.915589e+00</td>\n",
-       "      <td>4.228915e-02</td>\n",
-       "      <td>1.842158e-03</td>\n",
-       "      <td>1.455253e+00</td>\n",
-       "      <td>3.536043e+00</td>\n",
-       "      <td>2.904243e-02</td>\n",
-       "      <td>1.102289e-01</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>std</th>\n",
-       "      <td>4.167331e-01</td>\n",
-       "      <td>1.116120e+00</td>\n",
-       "      <td>1.010079e+00</td>\n",
-       "      <td>9.827940e+00</td>\n",
-       "      <td>7.161846e+00</td>\n",
-       "      <td>1.889552e-01</td>\n",
-       "      <td>6.023703e-01</td>\n",
-       "      <td>2.308151e-01</td>\n",
-       "      <td>2.732653e-01</td>\n",
-       "      <td>1.857322e-01</td>\n",
-       "      <td>...</td>\n",
-       "      <td>6.272396e-01</td>\n",
-       "      <td>1.600321e+00</td>\n",
-       "      <td>1.627279e-01</td>\n",
-       "      <td>1.970207e+01</td>\n",
-       "      <td>3.520885e-01</td>\n",
-       "      <td>4.288082e-02</td>\n",
-       "      <td>2.485654e-01</td>\n",
-       "      <td>4.789894e-01</td>\n",
-       "      <td>8.255957e-02</td>\n",
-       "      <td>4.633803e-02</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>min</th>\n",
-       "      <td>0.000000e+00</td>\n",
-       "      <td>0.000000e+00</td>\n",
-       "      <td>0.000000e+00</td>\n",
-       "      <td>0.000000e+00</td>\n",
-       "      <td>0.000000e+00</td>\n",
-       "      <td>0.000000e+00</td>\n",
-       "      <td>0.000000e+00</td>\n",
-       "      <td>0.000000e+00</td>\n",
-       "      <td>0.000000e+00</td>\n",
-       "      <td>0.000000e+00</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.000000e+00</td>\n",
-       "      <td>0.000000e+00</td>\n",
-       "      <td>0.000000e+00</td>\n",
-       "      <td>0.000000e+00</td>\n",
-       "      <td>0.000000e+00</td>\n",
-       "      <td>0.000000e+00</td>\n",
-       "      <td>1.018182e-02</td>\n",
-       "      <td>-0.000000e+00</td>\n",
-       "      <td>0.000000e+00</td>\n",
-       "      <td>0.000000e+00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>25%</th>\n",
-       "      <td>0.000000e+00</td>\n",
-       "      <td>0.000000e+00</td>\n",
-       "      <td>1.000000e+00</td>\n",
-       "      <td>0.000000e+00</td>\n",
-       "      <td>1.000000e+00</td>\n",
-       "      <td>0.000000e+00</td>\n",
-       "      <td>0.000000e+00</td>\n",
-       "      <td>0.000000e+00</td>\n",
-       "      <td>0.000000e+00</td>\n",
-       "      <td>0.000000e+00</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.000000e+00</td>\n",
-       "      <td>0.000000e+00</td>\n",
-       "      <td>0.000000e+00</td>\n",
-       "      <td>0.000000e+00</td>\n",
-       "      <td>0.000000e+00</td>\n",
-       "      <td>0.000000e+00</td>\n",
-       "      <td>1.307692e+00</td>\n",
-       "      <td>3.238901e+00</td>\n",
-       "      <td>0.000000e+00</td>\n",
-       "      <td>7.142857e-02</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>50%</th>\n",
-       "      <td>0.000000e+00</td>\n",
-       "      <td>1.000000e+00</td>\n",
-       "      <td>1.000000e+00</td>\n",
-       "      <td>0.000000e+00</td>\n",
-       "      <td>2.000000e+00</td>\n",
-       "      <td>0.000000e+00</td>\n",
-       "      <td>0.000000e+00</td>\n",
-       "      <td>0.000000e+00</td>\n",
-       "      <td>0.000000e+00</td>\n",
-       "      <td>0.000000e+00</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.000000e+00</td>\n",
-       "      <td>0.000000e+00</td>\n",
-       "      <td>0.000000e+00</td>\n",
-       "      <td>0.000000e+00</td>\n",
-       "      <td>0.000000e+00</td>\n",
-       "      <td>0.000000e+00</td>\n",
-       "      <td>1.444444e+00</td>\n",
-       "      <td>3.506891e+00</td>\n",
-       "      <td>0.000000e+00</td>\n",
-       "      <td>1.034483e-01</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>75%</th>\n",
-       "      <td>0.000000e+00</td>\n",
-       "      <td>2.000000e+00</td>\n",
-       "      <td>2.000000e+00</td>\n",
-       "      <td>0.000000e+00</td>\n",
-       "      <td>3.000000e+00</td>\n",
-       "      <td>0.000000e+00</td>\n",
-       "      <td>0.000000e+00</td>\n",
-       "      <td>0.000000e+00</td>\n",
-       "      <td>0.000000e+00</td>\n",
-       "      <td>0.000000e+00</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.000000e+00</td>\n",
-       "      <td>1.000000e+00</td>\n",
-       "      <td>0.000000e+00</td>\n",
-       "      <td>0.000000e+00</td>\n",
-       "      <td>0.000000e+00</td>\n",
-       "      <td>0.000000e+00</td>\n",
-       "      <td>1.615385e+00</td>\n",
-       "      <td>3.796218e+00</td>\n",
-       "      <td>0.000000e+00</td>\n",
-       "      <td>1.428571e-01</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>max</th>\n",
-       "      <td>1.000000e+00</td>\n",
-       "      <td>3.000000e+00</td>\n",
-       "      <td>1.710000e+02</td>\n",
-       "      <td>2.011000e+03</td>\n",
-       "      <td>8.198000e+03</td>\n",
-       "      <td>1.000000e+00</td>\n",
-       "      <td>1.360000e+02</td>\n",
-       "      <td>1.000000e+00</td>\n",
-       "      <td>1.000000e+00</td>\n",
-       "      <td>1.000000e+00</td>\n",
-       "      <td>...</td>\n",
-       "      <td>3.800000e+01</td>\n",
-       "      <td>1.360000e+02</td>\n",
-       "      <td>1.000000e+00</td>\n",
-       "      <td>8.367000e+03</td>\n",
-       "      <td>1.310000e+02</td>\n",
-       "      <td>1.000000e+00</td>\n",
-       "      <td>5.000000e+00</td>\n",
-       "      <td>6.570554e+00</td>\n",
-       "      <td>9.545455e-01</td>\n",
-       "      <td>1.000000e+00</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>8 rows × 26 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "              label  url_length_cat      num_dots    num_digits  \\\n",
-       "count  6.995056e+06    6.995056e+06  6.995056e+06  6.995056e+06   \n",
-       "mean   2.237147e-01    1.443553e+00  1.546945e+00  1.634359e+00   \n",
-       "std    4.167331e-01    1.116120e+00  1.010079e+00  9.827940e+00   \n",
-       "min    0.000000e+00    0.000000e+00  0.000000e+00  0.000000e+00   \n",
-       "25%    0.000000e+00    0.000000e+00  1.000000e+00  0.000000e+00   \n",
-       "50%    0.000000e+00    1.000000e+00  1.000000e+00  0.000000e+00   \n",
-       "75%    0.000000e+00    2.000000e+00  2.000000e+00  0.000000e+00   \n",
-       "max    1.000000e+00    3.000000e+00  1.710000e+02  2.011000e+03   \n",
-       "\n",
-       "       num_special_chars   url_keyword  num_underbar  \\\n",
-       "count       6.995056e+06  6.995056e+06  6.995056e+06   \n",
-       "mean        2.663572e+00  3.707890e-02  4.500550e-02   \n",
-       "std         7.161846e+00  1.889552e-01  6.023703e-01   \n",
-       "min         0.000000e+00  0.000000e+00  0.000000e+00   \n",
-       "25%         1.000000e+00  0.000000e+00  0.000000e+00   \n",
-       "50%         2.000000e+00  0.000000e+00  0.000000e+00   \n",
-       "75%         3.000000e+00  0.000000e+00  0.000000e+00   \n",
-       "max         8.198000e+03  1.000000e+00  1.360000e+02   \n",
-       "\n",
-       "       extract_consecutive_numbers        number         upper  ...  \\\n",
-       "count                 6.995056e+06  6.995056e+06  6.995056e+06  ...   \n",
-       "mean                  5.646374e-02  8.128041e-02  3.577641e-02  ...   \n",
-       "std                   2.308151e-01  2.732653e-01  1.857322e-01  ...   \n",
-       "min                   0.000000e+00  0.000000e+00  0.000000e+00  ...   \n",
-       "25%                   0.000000e+00  0.000000e+00  0.000000e+00  ...   \n",
-       "50%                   0.000000e+00  0.000000e+00  0.000000e+00  ...   \n",
-       "75%                   0.000000e+00  0.000000e+00  0.000000e+00  ...   \n",
-       "max                   1.000000e+00  1.000000e+00  1.000000e+00  ...   \n",
-       "\n",
-       "       subdomain_count    path_depth     has_query  query_length  \\\n",
-       "count     6.995056e+06  6.995056e+06  6.995056e+06  6.995056e+06   \n",
-       "mean      2.660177e-01  6.056849e-01  2.722137e-02  1.915589e+00   \n",
-       "std       6.272396e-01  1.600321e+00  1.627279e-01  1.970207e+01   \n",
-       "min       0.000000e+00  0.000000e+00  0.000000e+00  0.000000e+00   \n",
-       "25%       0.000000e+00  0.000000e+00  0.000000e+00  0.000000e+00   \n",
-       "50%       0.000000e+00  0.000000e+00  0.000000e+00  0.000000e+00   \n",
-       "75%       0.000000e+00  1.000000e+00  0.000000e+00  0.000000e+00   \n",
-       "max       3.800000e+01  1.360000e+02  1.000000e+00  8.367000e+03   \n",
-       "\n",
-       "       query_param_count  url_shorteners  compression_ratio       entropy  \\\n",
-       "count       6.995056e+06    6.995056e+06       6.995056e+06  6.995056e+06   \n",
-       "mean        4.228915e-02    1.842158e-03       1.455253e+00  3.536043e+00   \n",
-       "std         3.520885e-01    4.288082e-02       2.485654e-01  4.789894e-01   \n",
-       "min         0.000000e+00    0.000000e+00       1.018182e-02 -0.000000e+00   \n",
-       "25%         0.000000e+00    0.000000e+00       1.307692e+00  3.238901e+00   \n",
-       "50%         0.000000e+00    0.000000e+00       1.444444e+00  3.506891e+00   \n",
-       "75%         0.000000e+00    0.000000e+00       1.615385e+00  3.796218e+00   \n",
-       "max         1.310000e+02    1.000000e+00       5.000000e+00  6.570554e+00   \n",
-       "\n",
-       "        digit_ratio  special_char_ratio  \n",
-       "count  6.995056e+06        6.995056e+06  \n",
-       "mean   2.904243e-02        1.102289e-01  \n",
-       "std    8.255957e-02        4.633803e-02  \n",
-       "min    0.000000e+00        0.000000e+00  \n",
-       "25%    0.000000e+00        7.142857e-02  \n",
-       "50%    0.000000e+00        1.034483e-01  \n",
-       "75%    0.000000e+00        1.428571e-01  \n",
-       "max    9.545455e-01        1.000000e+00  \n",
-       "\n",
-       "[8 rows x 26 columns]"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
+     "ename": "NameError",
+     "evalue": "name 'processed_train' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
+      "\u001b[31mNameError\u001b[39m                                 Traceback (most recent call last)",
+      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[2]\u001b[39m\u001b[32m, line 1\u001b[39m\n\u001b[32m----> \u001b[39m\u001b[32m1\u001b[39m \u001b[43mprocessed_train\u001b[49m.describe()\n",
+      "\u001b[31mNameError\u001b[39m: name 'processed_train' is not defined"
+     ]
     }
    ],
    "source": [
     "processed_train.describe()"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'processed_train' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
+      "\u001b[31mNameError\u001b[39m                                 Traceback (most recent call last)",
+      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[1]\u001b[39m\u001b[32m, line 4\u001b[39m\n\u001b[32m      1\u001b[39m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mmatplotlib\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mpyplot\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mas\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mplt\u001b[39;00m\n\u001b[32m      2\u001b[39m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mseaborn\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mas\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01msns\u001b[39;00m\n\u001b[32m----> \u001b[39m\u001b[32m4\u001b[39m desc = \u001b[43mprocessed_train\u001b[49m.describe()\n\u001b[32m      6\u001b[39m plt.figure(figsize=(\u001b[32m12\u001b[39m, \u001b[32m6\u001b[39m))\n\u001b[32m      7\u001b[39m sns.barplot(data=desc.T[[\u001b[33m'\u001b[39m\u001b[33mmean\u001b[39m\u001b[33m'\u001b[39m, \u001b[33m'\u001b[39m\u001b[33mstd\u001b[39m\u001b[33m'\u001b[39m, \u001b[33m'\u001b[39m\u001b[33mmin\u001b[39m\u001b[33m'\u001b[39m, \u001b[33m'\u001b[39m\u001b[33mmax\u001b[39m\u001b[33m'\u001b[39m]])\n",
+      "\u001b[31mNameError\u001b[39m: name 'processed_train' is not defined"
+     ]
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "\n",
+    "desc = processed_train.describe()\n",
+    "\n",
+    "plt.figure(figsize=(12, 6))\n",
+    "sns.barplot(data=desc.T[['mean', 'std', 'min', 'max']])\n",
+    "plt.title('Feature Statistics')\n",
+    "plt.xticks(rotation=45)\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 11,
@@ -3248,12 +2601,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
     "import tensorflow as tf\n",
-    "from tensorflow.keras.layers import Dense, Dropout, BatchNormalization\n",
+    "from tensorflow.keras.layers import Dense\n",
     "\n",
     "def build_model(input_dim, learning_rate=0.001):\n",
     "    \"\"\"\n",

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("");
@@ -26,81 +28,90 @@ const UrlPredictor = () => {
     }
   };
 
-  // 모델 정보 정의 (title + 키)
   const models = [
-    { key: "old_model", title: "🧠 기존 모델 (Ho)" },
-    { key: "new_model", title: "🚀 개선 모델 (Jun)" },
+    { key: "model1", title: "HO 모델" },
+    { key: "model2", title: "Jun 모델" },
   ];
 
   return (
-    <div className="min-h-screen bg-gray-100 p-6">
-      {!results ? (
-        <div className="flex justify-center items-center h-full">
-          <form onSubmit={handleSubmit} className="flex gap-4 w-full max-w-2xl">
+    <div className="min-h-screen bg-blue-50 p-8">
+      <div className="grid grid-cols-1 md:grid-cols-2 gap-8 h-full">
+
+        {/* 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 */}
-          <div className="flex flex-col gap-4">
-            {models.map((model) => {
+        {/* 오른쪽 결과창 */}
+        <div className="flex flex-col gap-6">
+          {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">
-                  <h2 className="text-lg font-bold mb-2">{model.title}</h2>
-                  <p>
-                    악성 확률: <strong>{(data.prob * 100).toFixed(2)}%</strong>
+                <motion.div
+                  key={model.key}
+                  initial={{ opacity: 0, y: 30 }}
+                  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"}>
-                      {data.malicious ? "⚠️ 악성 URL" : "✅ 정상 URL"}
+                    <strong
+                      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>
   );
 };
 
-export default UrlPredictor;
+export default UrlPredictor;

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"
+  ]
+}