Enhancing Cyber Security: A Holistic Strategy for Advanced Malicious Website Prediction Using AdaBoost Algorithm

The rising threat of malicious websites presents a significant cybersecurity challenge, requiring advanced detection methods to safeguard users. While traditional approaches like rule-based systems and machine learning models such as Random Forest and support vector machine (SVM) have shown progress, they often struggle with trade-offs between precision and recall. This studyintroduces a four-step methodology designed to overcome these limitations by using the AdaBoost algorithm for enhanced malicious website detection. The proposed methodology comprises four key steps: (1) Dataset Acquisition—data is sourced from the "Malicious and Benign Webpages" dataset on Kaggle; (2) Data Preprocessing—the dataset undergoes rigorous preprocessing, including data cleaning and feature selection to prepare the dataset for model training and to enhance model accuracy;(3) Model Implementation—the AdaBoost algorithm, known for its robustness in ensemble learning, is employed to train the model on the preprocessed data; and (4) Model Evaluation—the model's performance is assessed using precision, recall, F1-score, and accuracy metrics. Our findings demonstrate that AdaBoost achieves a precision of 86.17% and an accuracy of 78.45%, offering abalanced trade-off between precision and recall compared to existing models, which typically emphasize one at the expense of the other. The high precision of AdaBoost underscores its reliability in real-world applications, where minimizing false positives is crucial. This study provides empirical support for AdaBoost's effectiveness and highlights the necessity of advanced machine learningtechniques and feature selection strategies in strengthening cybersecurity.