Evaluating the Effectiveness of Machine Learning in Phishing Detection

Abstract

Phishing attacks continue to be a significant threat to organizations and individuals, leading to data breaches, financial loss, and reputational damage. This research paper evaluates the effectiveness of machine learning (ML) techniques in detecting phishing attempts across various communication channels, including emails, websites, and social media platforms. We examine a range of ML algorithms, including supervised learning methods like decision trees, support vector machines, and neural networks, as well as unsupervised approaches and ensemble methods. Through a comprehensive analysis of existing literature, case studies, and empirical experiments, we assess the performance metrics of these models, such as accuracy, precision, recall, and F1 score. Additionally, we explore the challenges associated with phishing detection, including the evolving tactics of cybercriminals, data quality issues, and the need for real-time detection capabilities. Our findings indicate that while machine learning significantly enhances phishing detection rates compared to traditional methods, ongoing adaptation and continuous training are crucial to maintaining effectiveness against sophisticated phishing schemes. The paper concludes with recommendations for improving machine learning models in phishing detection and the importance of integrating these technologies with user education and awareness initiatives to create a holistic defense strategy.