A Machine Learning for Anomaly and Data Breach Detection System Using Python
Cherrita Vijitphu, Nattavee Utakrit · 2025
This research presents the development of an anomaly and data breach detection system using Python to analyze internet traffic logs. When comparing various machine learning algorithms, it was found that Random Forest is highly effective and excels at identifying important features, which helps in clearly identifying attack detection factors. This system includes a Power BI dashboard displayed on a web window to provide actionable insights to executives and security administrators. Expert evaluations confirmed an accuracy of 90.67% along with high user satisfaction scores. This system enhances cybersecurity measures, reduces the risk of data breaches, and effectively monitors network traffic to safeguard organizational data.