Optimizing Phishing Threat Detection: A Comprehensive Study of Advanced Bagging Techniques and Optimization Algorithms in Machine Learning

Bagging constitutes a prominent ensemble learning technique in contemporary machine learning. With this process, various instances of the base model are trained using various subsets of the training data that are extracted by bootstrapping. The resulting models are then aggregated, often through voting in a classification problem, to enhance performance and predictive power. Recent advances in bagging techniques include variants such as Random Forests, which introduce additional randomness by selecting a random subset of features in each partition and boosting algorithms that iteratively optimize the model's focus on misclassified instances. The efficacy of these strategies in enhancing the generality and adaptability of machine learning models has been impressive. There are many studies that confirm the ability of ensemble learning models to detect phishing attacks. However, the techniques used by these models that have enhanced their detection capabilities have not been highlighted. The study reached important results in terms of accuracy of up to 97% through the random forest model and the Particle swarm optimization algorithm. This study seeks to contribute to advancing the field of cybersecurity by providing a robust and interpretable machine learning-based classifier that can be integrated into a framework to detect phishing attacks by distinguishing between legitimate URLs and phishing URLs.