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PhishBusters

Our team, PhishBusters, specializes in stopping phishing attacks.

Phishing Call

Team Members

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 python@3.12
    • Install Pipenv and dependencies:
    pip3 install pipenv
pipenv install

  4. Run Python Environment:

    pipenv shell

  5. Start Server:

    npm run server:dev

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.

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

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