Machine learning with boosting algorithms is becoming increasingly important to fight phishing emails. Researchers are improving these algorithms by using better features, fine-tuning parameters, combining models, and leveraging deep learning techniques like transfer learning. This will help machine learning better protect users from evolving phishing attacks.

Phishing is a major online danger that includes deceiving people through false emails to steal sensitive information like passwords and financial data. As attackers become more sophisticated, the role of machine learning in identifying phishing emails before they land in users' inboxes is growing in importance.

Boosting techniques are frequently used in the field of machine learning to detect phishing attacks. Combining several "weaker" classifiers in order to enhance the performance of a classifier. AdaBoost, Gradient Boosting, and XGBoost are widely used boosting techniques for identifying phishing attacks.

Although boosting algorithms have been successful in identifying phishing attempts, there is room for improving their efficiency even more. Listed below are various approaches that researchers have explored to improve boosting algorithms for better detection of phishing emails.

Methods for generating additional input features to improve machine learning algorithms. Data such as word frequency, character types, and details of URLs/attachments in emails are crucial inputs for every machine learning model. Carefully selecting advantageous attributes through feature engineering can greatly improve results. Listed characteristics that are effective include prohibited keywords, suspicious website domains, and communication connections between both parties.

Adjusting parameters to optimise performance to its maximum level. Boosting algorithms are equipped with various hyperparameters that control elements like learning rate, number of trees/iterations, maximum tree depth, and minimum sample split. Improving the hyperparameters with grid search or Bayesian optimisation can boost the boosting algorithms' capability to learn better from the training data.

Methods in which various models are merged to enhance overall effectiveness. Utilising techniques like stacking has shown promise with different boosting models. Every model focuses on different aspects of the problem, and combining them maximises their combined strengths. This enhances the overall detection rates beyond what single models accomplish.

Deep learning techniques like transfer learning can be used to enhance the performance of boosting algorithms. Transfer learning is a method that allows models trained on a large amount of general domain text to be adjusted for the task of detecting phishing. This effectively enhances the model's comprehension of phishing emails by equipping it with a wider range of language knowledge.

Continuous improvement of machine learning defences is crucial as phishing attacks continue to develop. Boosting algorithms have demonstrated their efficiency in detecting phishing attacks, and by implementing meticulous feature engineering, hyperparameter tuning, ensemble techniques, and transfer learning, their effectiveness can be enhanced to a greater extent. This enables machine learning to have a more significant impact on safeguarding users against advanced phishing emails.

The concept of transfer learning

Models that have been trained on a large amount of general domain text can be adjusted for the task of detecting phishing through transfer learning. This effectively enhances the model's comprehension of phishing emails by equipping it with a wider range of language knowledge.

Continuous improvement of machine learning defences is crucial as phishing attacks continue to develop. Boosting algorithms have demonstrated their efficiency in detecting phishing attacks, and by implementing meticulous feature engineering, hyperparameter tuning, ensemble techniques, and transfer learning, their effectiveness can be enhanced to a greater extent. This enables machine learning to have a more significant impact on safeguarding users against advanced phishing emails.

Authors: Catalin Bondari & Bohdan Boiprav