An Explainable AI based Cyber Threat Detection Model to Minimize Phishing Attacks and Their Effect

Abstract

Phishing and social engineering rank among the most significant facilitators of cyber-crime, which encompasses data breaches, cyber-attacks, ransomware schemes, and denial of service incidents. A robust threat detection model is essential for reducing the risk of phishing attacks, as these types of attacks are frequently discussed in dark web forums and are thus widely adopted. It has been noted that these attacks can have severe direct or indirect effects on our assets, with the resulting financial damage closely linked to their occurrence. In this research, two methodologies are introduced: the first is an explainable AI (XAI) model specifically designed to assess cyber-risks associated with correlated phishing threats. The second is a hybrid approach referred to as a classifier ensemble, which employs a combination of three top-performing machine learning models through ensemble learning and categorizes them based on several high-impact parameters related to model performance. Ultimately, both proposed models have been compared and evaluated across multiple factors. The proposed model in addition carried out a precision of 97.5%, consider of 98.7%, and F1-score of 98.1%, with an average development of about 2–3% in comparison with baseline models. SHAP evaluation recognized Page Rank, URL Length, Number of Hyperlinks, Domain Age, Google Index, and Phishing Hints because the maximum influential capabilities affecting phishing prediction, while LIME supplied instance-stage reasons to enhance transparency and analyst confidence. The experimental findings display that integrating explainable artificial intelligence with ensemble getting to know appreciably improves phishing detection accuracy whilst simultaneously improving version interpretability, making the proposed framework appropriate for deployment in realistic cybersecurity environments.