EXPLICATE: Enhancing Phishing Detection through Explainable AI and LLM-Powered Interpretability
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arXiv
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| Format: | Preprint |
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2025
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| author | Lim, Bryan Huerta, Roman Sotelo, Alejandro Quintela, Anthonie Kumar, Priyanka |
| author_facet | Lim, Bryan Huerta, Roman Sotelo, Alejandro Quintela, Anthonie Kumar, Priyanka |
| contents | Sophisticated phishing attacks have emerged as a major cybersecurity threat, becoming more common and difficult to prevent. Though machine learning techniques have shown promise in detecting phishing attacks, they function mainly as "black boxes" without revealing their decision-making rationale. This lack of transparency erodes the trust of users and diminishes their effective threat response. We present EXPLICATE: a framework that enhances phishing detection through a three-component architecture: an ML-based classifier using domain-specific features, a dual-explanation layer combining LIME and SHAP for complementary feature-level insights, and an LLM enhancement using DeepSeek v3 to translate technical explanations into accessible natural language. Our experiments show that EXPLICATE attains 98.4 % accuracy on all metrics, which is on par with existing deep learning techniques but has better explainability. High-quality explanations are generated by the framework with an accuracy of 94.2 % as well as a consistency of 96.8\% between the LLM output and model prediction. We create EXPLICATE as a fully usable GUI application and a light Chrome extension, showing its applicability in many deployment situations. The research shows that high detection performance can go hand-in-hand with meaningful explainability in security applications. Most important, it addresses the critical divide between automated AI and user trust in phishing detection systems. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_20796 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | EXPLICATE: Enhancing Phishing Detection through Explainable AI and LLM-Powered Interpretability Lim, Bryan Huerta, Roman Sotelo, Alejandro Quintela, Anthonie Kumar, Priyanka Cryptography and Security Artificial Intelligence Sophisticated phishing attacks have emerged as a major cybersecurity threat, becoming more common and difficult to prevent. Though machine learning techniques have shown promise in detecting phishing attacks, they function mainly as "black boxes" without revealing their decision-making rationale. This lack of transparency erodes the trust of users and diminishes their effective threat response. We present EXPLICATE: a framework that enhances phishing detection through a three-component architecture: an ML-based classifier using domain-specific features, a dual-explanation layer combining LIME and SHAP for complementary feature-level insights, and an LLM enhancement using DeepSeek v3 to translate technical explanations into accessible natural language. Our experiments show that EXPLICATE attains 98.4 % accuracy on all metrics, which is on par with existing deep learning techniques but has better explainability. High-quality explanations are generated by the framework with an accuracy of 94.2 % as well as a consistency of 96.8\% between the LLM output and model prediction. We create EXPLICATE as a fully usable GUI application and a light Chrome extension, showing its applicability in many deployment situations. The research shows that high detection performance can go hand-in-hand with meaningful explainability in security applications. Most important, it addresses the critical divide between automated AI and user trust in phishing detection systems. |
| title | EXPLICATE: Enhancing Phishing Detection through Explainable AI and LLM-Powered Interpretability |
| topic | Cryptography and Security Artificial Intelligence |
| url | https://arxiv.org/abs/2503.20796 |