A comprehensive .NET Core client library for Keycloak that provides seamless integration with Keycloak's authentication and authorization services. This library offers a robust implementation of Keycloak's REST API, including support for OpenID Connect, OAuth 2.0, and User-Managed Access (UMA 2.0).

🛡️ Detect fraudulent credit card transactions with CardShield AI, an advanced machine learning pipeline utilizing SMOTE and optimized classification models.

🛡️ CardShield AI – Fraud Identification System Advanced credit card fraud detection system leveraging machine learning, SMOTE for imbalance handling, optimized Random Forest, feature scaling, and an interactive Streamlit app for single and batch transaction predictions.

💴 A machine learning project that detects fraudulent credit card transactions using classification algorithms. Includes data preprocessing, EDA, model training & evaluation with techniques like Random Forest, Logistic Regression, and SMOTE for class imbalance. Built for secure financial insights and real-world fraud detection use cases.

The Credit Card Fraud Detection project utilizes machine learning algorithms to identify and prevent fraudulent credit card transactions. Project Includes Source Code, PPT, Synopsis, Report, Documents, Base Research Paper & Video tutorials

Machine learning fraud detection system using ResNet, SVM and AdaBoost on the UCI Credit Card dataset.

Smart Fraud Guard Wallet is an intelligent fintech mobile application designed to protect users from financial fraud using advanced artificial intelligence and cybersecurity techniques.

FraudDetectAI is an advanced credit card fraud detection system built with XGBoost and Hybrid SMOTE Sampling (Oversampling + Undersampling). This project tackles highly imbalanced datasets, ensuring strong fraud detection accuracy while minimizing overfitting risks.

An ML-based anomaly detection system that identifies fraudulent credit card transactions using Isolation Forest and Random Forest classifiers. Achieves 100% precision and 88.9% recall on 284,807 real transactions with a 94.1% F1-score.

Aegis Security Environment is an enterprise-grade, multi-layered security platform designed to protect mobile banking applications from fraud and unauthorized access. It implements advanced cryptographic protocols, device fingerprinting, and policy-based security enforcement to ensure end-to-end protection of financial transactions.

Machine Learning pipeline for financial transaction fraud detection. Incorporates SMOTE, ensemble models, neural networks.

a machine learning application for real-time credit card fraud detection using machine learning models trained on synthetic and European transaction datasets.

An end-to-end machine learning pipeline for real-time financial fraud detection. Leverages advanced feature engineering, ensemble models, and handles severely imbalanced datasets (0.13% fraud rate). Achieves 99.83% PR-AUC with automated ETL pipeline and production-ready fraud detection capabilities.

Flutter aplikace pro ochranu před online finančními podvody. Oficiální kontakty bank, blokace karty, krizové postupy. Offline. MIT.

Syntropy Security's comprehensive security audit of the Online Banking Management System v1.0. Our assessment concludes that the application in its current state poses unacceptable risk to the organization. We identified five (5) critical security failures that would cause catastrophic financial loss and total operational paralysis if deployed.

Comprehensive personal finance platform. Track accounts, manage budgets, monitor investments, and achieve financial goals with bank-level security.

💥 Identify critical vulnerabilities in the SourceCodester Online Banking System v1.0, ensuring financial security and awareness of unpatched risks.