Feature Engineering and Explainable Artificial Intelligence for Phishing Website Detection: A Systematic Literature Review

Detecting phishing websites is a rapidly evolving field aimed at identifying and mitigating cyberattacks targeting individuals, organisations, and governments. Ongoing progress in artificial intelligence (AI) has the potential to revolutionise phishing detection by enhancing model accuracy and improving transparency through explainable AI (XAI). However, significant challenges remain, particularly in integrating feature engineering with XAI to address sophisticated phishing strategies, including zero-day attacks, that evade traditional detection mechanisms. To overcome these challenges, this paper examines the impact of feature engineering and XAI in phishing detection, emphasising their ability to enhance accuracy while providing interpretability. By integrating feature extraction with interpretable models, these techniques improve decision-making transparency and system robustness. This paper presents the first systematic literature review (SLR) focusing on the impact of feature engineering and XAI on state-of-the-art phishing detection approaches. Additionally, it identifies critical research gaps and challenges, including scalability issues, the evolution of phishing techniques, and balancing complexity with interpretability. The findings provide valuable academic insights while offering practical recommendations for developing accurate and interpretable phishing detection systems, aiding organisations in strengthening cybersecurity measures.