Abstract

The increasing reliance on digital platforms in office environments has led to a rise in cyber threats and online criminal activities. To address these challenges, this project proposes a comprehensive system for crime detection and malicious URL management within office webpages. The system leverages the power of ensemble machine learning algorithms, including Decision Tree Classifier, Random Forest Classifier, AdaBoost Classifier, KN Neighbors Classifier, SGD Classifier, Extra Tree Classifier, and Gaussian Naive Bayes, to enhance the accuracy and efficiency of the detection process.