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README.md

@@ -1,8 +1,8 @@
 ---
 title: Webphishing
-emoji: 😻
+emoji: 🐨
 colorFrom: green
-colorTo: indigo
+colorTo: pink
 sdk: gradio
 sdk_version: 5.29.1
 app_file: app.py

app.py

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+import gradio as gr
+from urllib.parse import urlparse
+import re
+import numpy as np
+import requests
+from bs4 import BeautifulSoup
+import whois
+from datetime import datetime
+import pandas
+import pickle
+import os
+from dotenv import load_dotenv
+
+# Load environment variables from .env
+load_dotenv()
+
+MOZ_ACCESS_ID = os.getenv("MOZ_ACCESS_ID")
+MOZ_SECRET_KEY = os.getenv("MOZ_SECRET_KEY")
+SERPAPI_KEY = os.getenv("SERPAPI_KEY")
+
+
+with open("phishing_svm_model.pkl", "rb") as f:
+    svm_pipeline = pickle.load(f)
+with open("phishing_knn_model.pkl", "rb") as f:
+    knn_pipeline = pickle.load(f)
+with open("phishing_rf_model.pkl", "rb") as f:
+    rf_pipeline = pickle.load(f)
+
+
+
+
+# Map features to their source
+feature_sources = {
+    # Auto-extracted features
+    'length_url': 'Calculated from URL', 'length_hostname': 'Calculated from URL', 'ip': 'Calculated from URL', 'nb_dots': 'Calculated from URL',
+    'nb_qm': 'Calculated from URL', 'nb_eq': 'Calculated from URL', 'nb_slash': 'Calculated from URL', 'nb_www': 'Calculated from URL',
+    'ratio_digits_url': 'Calculated from URL', 'ratio_digits_host': 'Calculated from URL', 'tld_in_subdomain': 'Calculated from URL',
+    'prefix_suffix': 'Calculated from URL', 'shortest_word_host': 'Calculated from URL', 'longest_words_raw': 'Calculated from URL',
+    'longest_word_path': 'Calculated from URL', 'phish_hints': 'Calculated from URL',
+    # API-extracted features
+    'domain_age': 'API', 'google_index': 'API', 'page_rank': 'API',
+    'empty_title': 'API', 'domain_in_title': 'API'
+  
+}
+
+
+all_features = [
+    'length_url', 'length_hostname', 'ip', 'nb_dots', 'nb_qm', 'nb_eq',
+    'nb_slash', 'nb_www', 'ratio_digits_url', 'ratio_digits_host',
+    'tld_in_subdomain', 'prefix_suffix', 'shortest_word_host',
+    'longest_words_raw', 'longest_word_path', 'phish_hints',
+    'nb_hyperlinks', 'ratio_intHyperlinks', 'empty_title',
+    'domain_in_title', 'domain_age', 'google_index', 'page_rank'
+]
+
+auto_features = [
+    'length_url', 'length_hostname', 'ip', 'nb_dots', 'nb_qm', 'nb_eq',
+    'nb_slash', 'nb_www', 'ratio_digits_url', 'ratio_digits_host',
+    'tld_in_subdomain', 'prefix_suffix', 'shortest_word_host',
+    'longest_words_raw', 'longest_word_path', 'phish_hints'
+]
+
+manual_features = list(set(all_features) - set(auto_features))
+manual_features.sort()
+
+def extract_from_url(url):
+    parsed = urlparse(url)
+    hostname = parsed.hostname or ""
+    path = parsed.path or ""
+
+    features = {
+        'length_url': len(url),
+        'length_hostname': len(hostname),
+        'ip': 1 if re.fullmatch(r"(\d{1,3}\.){3}\d{1,3}", hostname) else 0,
+        'nb_dots': url.count('.'),
+        'nb_qm': url.count('?'),
+        'nb_eq': url.count('='),
+        'nb_slash': url.count('/'),
+        'nb_www': url.count('www'),
+        'ratio_digits_url': sum(c.isdigit() for c in url) / len(url) if len(url) > 0 else 0,
+        'ratio_digits_host': sum(c.isdigit() for c in hostname) / len(hostname) if len(hostname) > 0 else 0,
+        'tld_in_subdomain': int(any(tld in hostname.split('.')[:-1] for tld in ['com', 'net', 'org'])),
+        'prefix_suffix': int('-' in hostname),
+        'shortest_word_host': min((len(w) for w in hostname.split('.')), default=0),
+        'longest_words_raw': max((len(w) for w in url.split('/')), default=0),
+        'longest_word_path': max((len(w) for w in path.split('/')), default=0),
+        'phish_hints': int(any(k in url.lower() for k in ['secure', 'login', 'paypal', 'ebay', 'bank']))
+    }
+    # print(features)
+    return features
+
+def get_domain_age(domain):
+    try:
+        info = whois.whois(domain)
+        creation = info.creation_date
+        if isinstance(creation, list):
+            creation = creation[0]
+        age = (datetime.now() - creation).days if creation else 0
+        return age
+    except Exception as e:
+        return 0
+
+def get_title_features(url):
+    try:
+        res = requests.get(url, timeout=5)
+        soup = BeautifulSoup(res.content, "html.parser")
+        title = soup.title.string if soup.title else ""
+        hostname = urlparse(url).hostname or ""
+        return {
+            "empty_title": int(title.strip() == ""),
+            "domain_in_title": int(hostname.lower().split('.')[0] in title.lower()) if title else 0
+        }
+    except:
+        return {"empty_title": 1, "domain_in_title": 0}
+
+def get_page_rank(url):
+    # Uncomment if you have Moz API credentials
+    endpoint = f"https://lsapi.seomoz.com/v2/url_metrics"
+    headers = {"Content-Type": "application/json"}
+    response = requests.post(
+        endpoint,
+        json={"targets": [url]},
+        auth=(MOZ_ACCESS_ID, MOZ_SECRET_KEY),
+        headers=headers
+    )
+    return response.json()["results"][0]["page_authority"]
+  
+    # return 0  # Placeholder for demo
+
+def is_google_indexed(url):
+    # Uncomment if you have SerpAPI
+    search_url = f"https://serpapi.com/search?engine=google&q=site:{url}&api_key={SERPAPI_KEY}"
+    res = requests.get(search_url).json()
+    return 1 if res.get("organic_results") else 0
+
+    # return 0  # Placeholder for demo
+
+def predict_from_url(url, model_choice, *manual_inputs):
+    auto_vals = extract_from_url(url)
+    hostname = urlparse(url).hostname or ""
+
+    # API features
+    auto_vals['domain_age'] = get_domain_age(hostname)
+    auto_vals['page_rank'] = get_page_rank(url)
+    auto_vals['google_index'] = is_google_indexed(url)
+    title_feats = get_title_features(url)
+    auto_vals.update(title_feats)
+
+    manual_features_remaining = [f for f in manual_features if f not in auto_vals]
+    manual_vals = dict(zip(manual_features_remaining, manual_inputs))
+
+    # Build input
+    full_input = []
+    feature_rows = []
+    for f in all_features:
+        if f in auto_vals:
+            val = auto_vals[f]
+            source = feature_sources.get(f, "Auto")
+        elif f in manual_vals:
+            val = manual_vals[f]
+            source = "Manual"
+        else:
+            val = None
+            source = "Manual"
+        full_input.append(val)
+        feature_rows.append({"Feature": f, "Value": val, "Source": source})
+
+    X = np.array(full_input).reshape(1, -1)
+    # Model selection
+    if model_choice == "SVM":
+        prediction = svm_pipeline.predict(X)[0]
+    elif model_choice == "Random Forest":
+        prediction = rf_pipeline.predict(X)[0]
+    else:  # KNN
+        prediction = knn_pipeline.predict(X)[0]
+
+    result_str = "Phishing 🚨 (1)" if prediction == 1 else "Legitimate ✅ (0)"
+    df = pd.DataFrame(feature_rows)
+    return result_str, df
+
+
+# Manual features needed for input
+manual_inputs = [gr.Number(label=f"{f} (manual)") for f in manual_features if f not in [
+    'domain_age', 'page_rank', 'google_index', 'empty_title', 'domain_in_title'
+]]
+
+app = gr.Interface(
+    fn=predict_from_url,
+    inputs=[
+        gr.Text(label="Enter URL"),
+        gr.Dropdown(choices=["SVM", "KNN", "Random Forest"], label="Choose Model", value="KNN"),
+        *manual_inputs
+    ],
+    outputs=[
+        gr.Text(label="Prediction"),
+        gr.Dataframe(label="Calculated Features Table")
+    ],
+    title="🔍 Advanced URL Phishing Detector",
+    description="See all extracted and provided features, their values, and their source (Auto, API, Manual)."
+)
+
+app.launch(share=True, debug=True)

phishing_knn_model.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:84bece316404cc3935010ee07ce9aaa706cc48ab4473635abdf758146c5b2ecd
+size 132

phishing_model.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:6638b41ae2098992ec578a0b01d2b4e3c299b68e8d58c544c85d447b1c7942a9
+size 132

phishing_rf_model.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:ba881e19a2f8be43ee6eeb458b594bcec5387444af1aab2a48dbefea0b91abb1
+size 132

phishing_svm_model.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:be4ceae37d96deea0192dbea47fb1c6abde7817ff2ac5a4114585ef5472f893e
+size 131

requirements.txt

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+pandas
+joblib>=1.2
+gradio>=4.0
+scikit-learn>=1.2
+pandas>=1.4
+numpy>=1.21
+requests>=2.28
+beautifulsoup4>=4.11
+python-whois>=0.8
+python-dotenv

scaler.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:08a6f34d80224c50be00fd6dd9675361decf10508e64e9408f6fd89bd62f1c66
+size 129