PhishBusters

Our team, PhishBusters, specializes in stopping phishing attacks.

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Team Members

• changmi-kim
• Goohyun3436
• rladydqls99
• ddevkim

Mission

Our mission is to leverage AI technology to eliminate phishing phone calls, creating a safer world free from the economic distress caused by phone scams. We strive to build a cleaner and better society by developing innovative solutions to combat these fraudulent activities.

Project Structure

Front-End (FE)

The front-end part of the project is responsible for the user interface and user experience. It includes:

UI Design: Development of visually simple and user-friendly design.

• User Experience: Experience design aimed at providing an environment where users can easily interact.
• Voice Recording: Utilizing the Web Audio API to record user's voice and transmit it to the server.
• Mobile Compatibility: Development and testing considering compatibility on mobile platforms.

1. Navigate to the Client Folder

   cd client

2. Install Node.js:

   • Install Node.js and dependencies:

   brew install node

3. Install yarn

   brew install yarn --ignore-dependencies

4. Install dependencies:

   yarn install

5. Start Client Server:

   yarn dev

6. View In The Browser:

   localhost:5173

Back-End (BE)

The back-end part of the project handles the core functionalities and data processing. It includes:

• AI Algorithms: Implementation of deep learning models to detect and block phishing calls.
• API Services: Robust APIs to support front-end functionalities and third-party integrations.
• Speech to Text: Utilizing Google Cloud API to convert voice files into text.
• Predict Phishing Calls: Using trained models on phishing call datasets to predict and identify scam calls.

1. Navigate to the Server Folder

   cd server

2. Install Node.js:

   • Install Node.js and dependencies:

   brew install node
   npm ci

3. Install Python 3.12 and Pipenv

   • Install Python 3.12

   brew install [email protected]

   • Install Pipenv and dependencies:

   pip3 install pipenv
   pipenv install

4. Run Python Environment:

   pipenv shell

5. Start Server:

   npm run server:dev

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AI

Dataset

• "Voice Phishing": Financial Supervisory Service Voice Phishing Dataset
• "General": AI Hub Voice Dataset, AI Hub Ethics Verification Data

TF-IDF Model Vectorizer(TF-IDF Based Logistic Regression Model)

• Purpose: To emphasize the importance of specific words related to voice phishing in the text data. Operation: After TF-IDF transformation, multiply each w ord vector by the weights defined in weight_dict to improve classification accuracy.
• Pipeline Composition:
• Vectorizer: Use WeightedTfidfVectorizer to extract features from the text data.
• Classifier: Use LogisticRegression to classify the text as voice phishing or not.

SKT Brain's KoBERT Model

• KoBERT: A Transformer-based model that understands the meaning of words by considering the bidirectional context of the given text.

Combined Approach with the Two Models

• Combined Predict: Combine the predictions of the two models, taking into account their respective weights.
• Advantages:
• TF-IDF Model: Uses a statistical approach to evaluate the importance of keywords within the text, but may not capture the contextual relationships or hidden meanings of the words.
• KoBERT: Understands the meaning of language by considering the entire context, providing a strong advantage for data where context is crucial, such as voice calls.
• Weight Settings: Set the weight of the BERT model to 0.7 and the weight of the TF-IDF model to 0.3.

Retrospective

• Challenges: While aiming for a complementary development as described above, the performance was found to be inferior compared to the GPT model, indicating a need for further model training and additional research.

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This project is a result of a hackathon supported by Hanghae99.