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ML MODEL
Ransomware Infection Risk Predictor Inspiration Cybersecurity threats, especially ransomware, are increasing daily, but many users lack awareness of their risks. This inspired us to create a tool that uses machine learning to educate and empower users by predicting infection risks based on their online behaviors.
What We Learned The basics of applying machine learning models like Linear Regression for risk prediction. Integrating Streamlit to create interactive web applications. Challenges users face with ransomware and the importance of user education. How We Built It We used Python and the Streamlit framework to develop the project. A simple Linear Regression model was trained on dummy behavioral data, such as phishing emails clicked, antivirus use, and software update frequency, to predict infection risks. The app provides actionable safety tips based on the risk level.
Challenges Designing the model with limited data. Ensuring the user interface is intuitive and informative. Balancing simplicity with functionality to deliver real-time insights effectively.
