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import gradio as gr |
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import tensorflow as tf |
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import pickle |
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import numpy as np |
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import requests |
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from ProGPT import Conversation |
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with open('preprocessing_params.pkl', 'rb') as f: |
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preprocessing_params = pickle.load(f) |
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with open('fisher_information.pkl', 'rb') as f: |
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fisher_information = pickle.load(f) |
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with open('label_encoder.pkl', 'rb') as f: |
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label_encoder = pickle.load(f) |
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with open('url_tokenizer.pkl', 'rb') as f: |
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url_tokenizer = pickle.load(f) |
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with open('html_tokenizer.pkl', 'rb') as f: |
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html_tokenizer = pickle.load(f) |
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@tf.keras.utils.register_keras_serializable() |
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class EWCLoss(tf.keras.losses.Loss): |
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def __init__(self, model, fisher_information, importance=1.0, reduction='auto', name=None): |
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super(EWCLoss, self).__init__(reduction=reduction, name=name) |
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self.model = model |
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self.fisher_information = fisher_information |
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self.importance = importance |
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self.prev_weights = [layer.numpy() for layer in model.trainable_weights] |
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def call(self, y_true, y_pred): |
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standard_loss = tf.keras.losses.binary_crossentropy(y_true, y_pred) |
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ewc_loss = 0.0 |
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for layer, fisher_info, prev_weight in zip(self.model.trainable_weights, self.fisher_information, self.prev_weights): |
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ewc_loss += tf.reduce_sum(fisher_info * tf.square(layer - prev_weight)) |
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return standard_loss + (self.importance / 2.0) * ewc_loss |
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def get_config(self): |
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config = super().get_config() |
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config.update({ |
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'importance': self.importance, |
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'reduction': self.reduction, |
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'name': self.name, |
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}) |
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return config |
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@classmethod |
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def from_config(cls, config): |
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with open('fisher_information.pkl', 'rb') as f: |
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fisher_information = pickle.load(f) |
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return cls(model=None, fisher_information=fisher_information, **config) |
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model = tf.keras.models.load_model('new_phishing_detection_model.keras', |
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custom_objects={'EWCLoss': EWCLoss}) |
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ewc_loss = EWCLoss(model=model, fisher_information=fisher_information, importance=1000) |
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model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=0.0005), |
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loss=ewc_loss, |
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metrics=['accuracy', tf.keras.metrics.Precision(), tf.keras.metrics.Recall()]) |
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access_token = 'your_pro_gpt_access_token' |
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chatbot = Conversation(access_token) |
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def preprocess_input(input_text, tokenizer, max_length): |
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sequences = tokenizer.texts_to_sequences([input_text]) |
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padded_sequences = tf.keras.preprocessing.sequence.pad_sequences(sequences, maxlen=max_length, padding='post', truncating='post') |
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return padded_sequences |
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def get_prediction(input_text, input_type): |
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is_url = input_type == "URL" |
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if is_url: |
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input_data = preprocess_input(input_text, url_tokenizer, preprocessing_params['max_url_length']) |
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else: |
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input_data = preprocess_input(input_text, html_tokenizer, preprocessing_params['max_html_length']) |
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prediction = model.predict([input_data, input_data])[0][0] |
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return prediction |
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def fetch_latest_phishing_sites(): |
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try: |
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response = requests.get('https://data.phishtank.com/data/online-valid.json') |
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data = response.json() |
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return data[:5] |
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except Exception as e: |
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return [] |
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def phishing_detection(input_text, input_type): |
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prediction = get_prediction(input_text, input_type) |
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if prediction > 0.5: |
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return f"Warning: This site is likely a phishing site! ({prediction:.2f})" |
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else: |
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return f"Safe: This site is not likely a phishing site. ({prediction:.2f})" |
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def latest_phishing_sites(): |
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sites = fetch_latest_phishing_sites() |
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return [f"{site['url']}" for site in sites] |
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def chatbot_response(user_input): |
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response = chatbot.prompt(user_input) |
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return response |
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iface = gr.Interface( |
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fn=phishing_detection, |
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inputs=[gr.inputs.Textbox(lines=5, placeholder="Enter URL or HTML code"), gr.inputs.Radio(["URL", "HTML"], type="value", label="Input Type")], |
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outputs="text", |
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title="Phishing Detection with Enhanced EWC Model", |
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description="Check if a URL or HTML is Phishing", |
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theme="default" |
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) |
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iface.launch() |