PraisesPJMT/rs_model_cbn_kaduna

This project involves developing three risk scoring methods (Weighted Scoring Model, Logistic Regression Model, and Random Forest Model) using private datasets from the network logs of the Central Bank of Nigeria. The goal is to enhance the risk scoring model for cybersecurity incidents. # Development of an Enhanced Risk Scoring Model for Cybersecurity Incidents in Banks: A Case Study of the Central Bank of Nigeria ## Thesis Project > This project involves developing three risk scoring methods (Weighted Scoring Model, Logistic Regression Model, and Random Forest Model) using private datasets from the network logs of the Central Bank of Nigeria. The goal is to enhance the risk scoring model for cybersecurity incidents. ## Table of Contents - Development of an Enhanced Risk Scoring Model for Cybersecurity Incidents in Banks: A Case Study of the Central Bank of Nigeria - Thesis Project - Table of Contents - Introduction - Project Structure - Setup and Installation - Data Preprocessing - Model Development - Model Evaluation - Plots and Results - Saved Models and Tables - Acknowledgments - Contact ## Introduction > This project aims to improve the accuracy and reliability of risk scoring models for cybersecurity incidents in banks. The project involves: 1. Developing a Weighted Scoring Model. 2. Building and training Logistic Regression and Random Forest models. 3. Evaluating the performance of these models using various metrics. 4. Visualizing the results and comparing the models. ## Project Structure ``` ├── data │ ├── cybersecurity_incidents.csv # Dataset used for training and evaluation ├── models │ ├── logistic_regression_model.joblib # Trained Logistic Regression model │ ├── random_forest_model.joblib # Trained Random Forest model │ ├── weighted_scoring_weights.joblib # Weights for Weighted Scoring model ├── plots │ ├── feature_correlation_heatmap.png # Correlation heatmap of features │ ├── learning_curve_logistic_regression.png # Learning curve for Logistic Regression │ ├── learning_curve_random_forest.png # Learning curve for Random Forest │ ├── model_comparison_plots.png # Comparison plots for models │ ├── precision_recall_curve.png # Precision-Recall curve │ ├── risk_score_distribution_by_category.png # Risk score distribution by categ …