ARIOSE is a credit card fraud detection platform designed to safeguard your financial transactions with both precision and security. Leveraging the power of advanced machine learning algorithms and blockchain technology, ARIOSE ensures that your transactions are not only secure but also transparent and immutable. ARIOSE is a step into a future of credit card security where finances are protected with a platform that combines the best of AI and blockchain technology for security and peace of mind.
- Website Flow
- Problem Statement
- USPs
- Objective
- Solution
- Tech Stack
- Implementation
- Features
- Demo Video
- Challenges Faced
- Contributors
- Project Structure
- Implement machine learning models for detecting anomalies and fraud patterns.
- Develop a user interface for monitoring transactions and alerts.
- Integrate backend services for processing and analyzing transaction data.
- Ensure real-time fraud detection and alert generation.
- Include features for investigating and managing fraud cases.
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ARIOSE uses advanced machine learning algorithms, specifically the Isolation Forest, to detect anomalies and potential fraud in real-time.
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By leveraging blockchain technology, ARIOSE provides security for all credit card transactions. Blockchain's decentralized nature ensures that all transaction records are immutable and tamper-proof, guaranteeing the integrity of your financial data.
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ARIOSE employs blockchain technology to mask and secure user information.
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Each transaction is recorded on the blockchain which has a decentralized server on IPFS .
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With ARIOSE, user privacy is a top priority. Diamante ensures that personal and transaction data remain confidential and secure.
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The Isolation Forest algorithm is specifically designed to excel in anomaly detection, making it ideal for identifying fraudulent transactions. This algorithm isolates anomalies by randomly selecting a feature and then randomly selecting a split value between the maximum and minimum values of the selected feature.
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Our platform offers a sleek and intuitive interface that allows users to easily navigate and manage their transaction security. Real-time alerts and detailed analytics provide users with the information they need to act quickly and decisively.
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We offer round-the-clock customer support to assist users with any issues or questions they may have. Our dedicated support team ensures that users have a smooth and secure experience with ARIOSE.
To revolutionize credit card fraud detection by combining machine learning algorithms with the robust security of diamante blockchain technology, providing users with unparalleled protection, privacy.
In an era where digital transactions are increasingly susceptible to fraud, ARIOSE provides a solution to safeguard financial operations. Combining advanced machine learning techniques with blockchain technology, ARIOSE ensures the highest levels of security, transparency, and efficiency in detecting and preventing credit card fraud.
Frontend: HTML, CSS, Javascript, React.js
Framework: Bootstrap
Backend: Express.js, Node.js,Flask,Python
Blockchain: Diamante
Machine Learning : scikit-learn,numpy,pandas
Decentralized Server Used : IPFS
- Node.js
- Javascript
- Python(Jupyter notebook)
- Git
- React.js
- Diamante
- IPFS
- Diamante:To set up the backend, follow these steps:
- Clone the repository: git clone https://github.com/diamcircle/diamante-js-sdk-sample
- Navigate to the backend directory and install dependencies:\ cd diamante-js-sdk-sample/diamante-demo npm install
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IPFS: Download IPFS: Go to the IPFS installation page and download the appropriate version for your operating system
Diamante Blockchain is known for its high security, transparency, and efficiency in handling financial transactions. Integrating Diamante Blockchain into ARIOSE enhances the platform's fraud detection capabilities and provides a robust, immutable ledger for all transactions.
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Diamante Blockchain ensures that all transaction data is securely recorded and immutable, preventing unauthorized alterations and ensuring data integrity.
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Each transaction is transparently recorded on the Diamante Blockchain, allowing for easy auditing and verification, which builds user trust and complies with regulatory requirements.
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The blockchain's efficient consensus mechanism ensures fast transaction processing, which is crucial for real-time fraud detection and prevention.
ARIOSE leverages advanced machine learning algorithms to detect and prevent credit card fraud in real-time. The primary machine learning model used is the Isolation Forest algorithm, which excels in anomaly detection. This section outlines the usage, training, and prediction processes for the machine learning components within ARIOSE.
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Collect historical transaction data including features such as transaction amount, timestamp, location, merchant details, and user information.
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For supervised algorithms, label the data to indicate whether each transaction is legitimate or fraudulent.
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Remove duplicates, handle missing values, and correct any inconsistencies in the data.
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Create new features or modify existing ones to improve the model's predictive power (e.g., transaction frequency, average transaction amount).
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Scale the features to a standard range to ensure the model's performance is not biased by the scale of the input data.
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An unsupervised learning algorithm specifically designed for anomaly detection.Ideal for identifying fraudulent transactions by isolating anomalies from the normal transaction patterns.
Utilizes the Isolation Forest algorithm for real-time anomaly detection, ensuring immediate identification and prevention of fraudulent transactions.
Provides real-time alerts to users and administrators when a suspicious transaction is detected, enabling prompt action.
Every transaction is recorded on the Diamante Blockchain, creating an immutable ledger that prevents tampering and ensures data integrity.
Masks and secures user information using blockchain technology, ensuring confidentiality and reducing the risk of data breaches.
Features a sleek and intuitive dashboard that allows users to easily monitor transactions, view alerts, and access detailed analytics.
Provides comprehensive analytics and visualizations of transaction data, helping users understand patterns and detect anomalies.
Offers robust APIs for seamless integration with existing financial systems, ensuring comprehensive oversight without disrupting current operations.
Compatible with various payment gateways and financial platforms, facilitating widespread adoption.
Maintains a transparent and verifiable ledger of all transactions on the blockchain, building user trust and ensuring regulatory compliance.
Prioritizes user privacy with advanced cryptographic methods, ensuring personal and transaction data remain secure and confidential.
Provides round-the-clock customer support to assist users with any issues or questions, ensuring a smooth and secure experience.
Offers detailed documentation and tutorials to help users and developers understand and utilize the platform effectively.
Regularly updates the platform with new features, security patches, and improvements based on user feedback and technological advancements.
Incorporates user feedback into the development process, ensuring the platform evolves to meet user needs and industry standards.
Ensuring that sensitive transaction data used for machine learning is kept secure and private while being processed.
Implementing privacy-preserving machine learning techniques to comply with data protection regulations.
Integrating machine learning models with blockchain infrastructure seamlessly.
Ensuring that the outputs of machine learning models can be effectively utilized within the Web3 ecosystem.
Ensuring seamless interaction between the blockchain layer and the application layer.
Handling the potential performance bottlenecks of blockchain, such as transaction throughput and confirmation times.
Implementing solutions to scale the blockchain network as the number of users and transactions increases.
Managing repository access and permissions to ensure that team members have the necessary access while maintaining security.
Handling issues related to repository cloning, pushing, and pulling due to account or authentication problems.
Ensuring that all team members are using consistent version control practices to avoid conflicts and merge issues.
Resolving merge conflicts that arise from simultaneous development efforts.
Coordinating among team members to ensure smooth collaboration and avoid issues like code overwriting or accidental deletions.
Utilizing GitHub features like branches, pull requests, and issues effectively for team collaboration.
Setting up and configuring the development and production environments, including servers, databases, and blockchain nodes.
Ensuring that all components of the application are correctly configured and can communicate with each other.
Deploying the application on cloud platforms and ensuring that all services are up and running.
Handling deployment issues such as server downtime, configuration errors, and dependency management.
Integrating third-party APIs and services into the application and handling any compatibility or configuration issues.
Ensuring that the APIs are secure and perform well under load.
Identifying and fixing bugs that arise during development and testing phases.
Ensuring comprehensive testing of the application to detect and resolve issues early in the development process.
Maintaining up-to-date documentation to ensure that all team members are aware of the system architecture, configuration, and best practices.
Facilitating knowledge sharing and troubleshooting among team members to quickly resolve technical issues.
Anusha Arora: Made login page, and transaction pages and data processing and readme and ppt and integration of backend with ML model.
Aditi Mehta: Made the whole front end completely and including blockchain.
Arshiya Garg: Made a whole ML model and training dataset, data preprocessing and learned about IPFS and readme.
Vania Goel: Helping in front end, integrating ML and blockchain together using IPFS, Integrating APIs, deploying ML and blockchain in backend.
- project-root
- data
- raw
- processed
- external
- interim
- notebooks
- src
- __init__.py
- data
- __init__.py
- data_loader.py
- features
- __init__.py
- feature_engineering.py
- models
- __init__.py
- train_model.py
- predict_model.py
- visualization
- __init__.py
- visualize.py
- utils
- __init__.py
- utils.py
- tests
- __init__.py
- test_data_loader.py
- test_feature_engineering.py
- test_train_model.py
- test_predict_model.py
- scripts
- run_data_pipeline.py
- run_training.py
- run_prediction.py
- requirements.txt
- Dockerfile
- setup.py
- README.md
- .gitignore
- data