musahthegreat/CCTV-Anomaly-Intrusion-Detection-System

A Python/Streamlit application that watches video footage and automatically flags Loitering and Intrusion events in a restricted zone (e.g. a gate or entrance), built as a third-year Computer Science project (Security/Surveillance domain, Kenya context). # Mobile Money Fraud Detection System A Python/Streamlit application that predicts fraud risk for mobile-money (M-Pesa-style) transactions in real time, built as a third-year Computer Science project (Fintech / AI-ML domain, Kenya context). ## Problem It Solves Mobile money fraud in Kenya — SIM-swap account takeover, agent-till fraud, and "reversal scam" social engineering — currently relies mostly on manual, after-the-fact reporting. This system scores each transaction in real time using a Random Forest classifier trained on behavioral red flags (SIM/device change, receiver novelty, location deviation, transaction velocity, timing, and amount), so high-risk transactions can be flagged **before** money moves. ## Project Structure ``` fraud_app/ ├── train_model.py # Generates synthetic data + trains the model ├── app.py # Streamlit dashboard (the "app") ├── requirements.txt # Python dependencies ├── fraud_model.joblib # Trained model (generated by train_model.py) ├── transactions_sample.csv # Synthetic dataset (generated by train_model.py) └── README.md ``` ## Setup & Running 1. **Install dependencies** (Python 3.10+ recommended): ```bash pip install -r requirements.txt ``` 2. **Train the model** (only needed once, or whenever you want to regenerate the dataset/model): ```bash python train_model.py ``` This creates `fraud_model.joblib` and `transactions_sample.csv`. 3. **Launch the dashboard:** ```bash streamlit run app.py ``` Then open the URL Streamlit prints (usually `localhost`). ## Using the App - **Prediction tab:** Enter transaction details in the sidebar (amount, time, transaction type, receiver novelty, SIM/device-change flag, location deviation) and click **Analyze Transaction** to get a fraud risk score and a plain-English breakdown of which red flags triggered it. - **Fraud Analytics tab:** Explore the training dataset — fraud rate by hour, by transaction type, by SIM/device-change status …