Home»Computer Science» Predictive analysis for cybersecurity risk management using decision tree regression

Predictive analysis for cybersecurity risk management using decision tree regression

 Department:Computer Science  
 By:usericon MIke_Tech_World  

 Project ID: 9634
 Rating:  (5.0) votes: 1
   Price:₦5000
Abstract
This project presents a predictive analysis system for cybersecurity risk management utilizing a Decision Tree Regression model to assess the severity of potential vulnerabilities based on various input features. The methodology encompasses data collection, preprocessing, model development, evaluation, and deployment. Initially, the dataset, which includes attributes such as vendor_project, product, vulnerability_name, and CVSS score, was meticulously loaded, cleaned, and transformed to ensure compatibility with the machine learning model. The system leverages historical data to train the model, which is then rigorously evaluated using metrics like Mean Absolute Error and R-squared values to ensure predictive accuracy. Following successful training and validation, the model is integrated into a user-friendly web application developed with Flask, allowing stakeholders to input relevant data and receive real-time predictions regarding vulnerability severity. The implementation of cross-validation techniques and the visualization of the decision tree further enhance the model''s reliability and interpretability. This comprehensive approach not only aids organizations in identifying and prioritizing cybersecurity risks but also fosters proactive decision-making in threat mitigation strategies, ultimately contributing to a more secure digital environment....
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