VeriPhish: Bridging AI Explainability and Accuracy in Phishing Detection Through XAI and LLMs

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14610%2F25%3A00143083" target="_blank" >RIV/00216224:14610/25:00143083 - isvavai.cz</a>

Výsledek na webu

<a href="https://ieeexplore.ieee.org/document/11295116" target="_blank" >https://ieeexplore.ieee.org/document/11295116</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/ICCST63435.2025.11295116" target="_blank" >10.1109/ICCST63435.2025.11295116</a>

Jazyk výsledku

angličtina

Název v původním jazyce

VeriPhish: Bridging AI Explainability and Accuracy in Phishing Detection Through XAI and LLMs

Popis výsledku v původním jazyce

Phishing attacks have evolved into a significant cybersecurity threat, becoming more frequent and increasingly difficult to defend against. While machine learning techniques show promise for detecting these threats, they typically operate as "black boxes," providing little insight into their decision-making processes. This lack of transparency undermines user confidence and reduces the effectiveness of threat response. To address this challenge, we introduce VeriPhish, a framework designed to improve phishing detection through a three-part architecture. The framework combines a machine learning classifier using domain-specific features, a dual-explanation layer that integrates LIME and SHAP for detailed feature-level analysis, and an LLM enhancement module that leverages DeepSeek v3 to convert technical explanations into understandable natural language. Our experiments demonstrate that VeriPhish achieves 98.4 % accuracy across all tested metrics, performing comparably to existing deep learning methods while offering superior explainability. The framework provides explanations with 94.2% accuracy and shows 96.8 % consistency between the LLM-generated explanations and the model's predictions. VeriPhish is available as both a fully functional GUI application and a lightweight Chrome extension, demonstrating its versatility across various deployment contexts. This work shows that high detection accuracy can be successfully paired with effective explainability in security applications, addressing the critical gap between AI-driven systems and user trust in phishing detection.

Název v anglickém jazyce

VeriPhish: Bridging AI Explainability and Accuracy in Phishing Detection Through XAI and LLMs

Popis výsledku anglicky

Phishing attacks have evolved into a significant cybersecurity threat, becoming more frequent and increasingly difficult to defend against. While machine learning techniques show promise for detecting these threats, they typically operate as "black boxes," providing little insight into their decision-making processes. This lack of transparency undermines user confidence and reduces the effectiveness of threat response. To address this challenge, we introduce VeriPhish, a framework designed to improve phishing detection through a three-part architecture. The framework combines a machine learning classifier using domain-specific features, a dual-explanation layer that integrates LIME and SHAP for detailed feature-level analysis, and an LLM enhancement module that leverages DeepSeek v3 to convert technical explanations into understandable natural language. Our experiments demonstrate that VeriPhish achieves 98.4 % accuracy across all tested metrics, performing comparably to existing deep learning methods while offering superior explainability. The framework provides explanations with 94.2% accuracy and shows 96.8 % consistency between the LLM-generated explanations and the model's predictions. VeriPhish is available as both a fully functional GUI application and a lightweight Chrome extension, demonstrating its versatility across various deployment contexts. This work shows that high detection accuracy can be successfully paired with effective explainability in security applications, addressing the critical gap between AI-driven systems and user trust in phishing detection.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10200 - Computer and information sciences

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2025

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

2025 IEEE International Carnahan Conference on Security Technology (ICCST)

ISBN

9798331523190

ISSN

—

e-ISSN

—

Počet stran výsledku

6

Strana od-do

1-6

Název nakladatele

IEEE

Místo vydání

New York, NY

Místo konání akce

San Antonio, TX, USA

Datum konání akce

13. 10. 2025

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

001710575400035