Treffer: AntiPhishX: An AI-driven service-oriented ensemble framework for detecting phishing and ai-powered phishing attacks.

The internet has become an essential societal utility, providing opportunities for both legitimate and illegitimate users. Cyberattacks, including phishing Uniform Resource Locator (URL) attacks, have emerged as a significant cybersecurity concern, especially with the increasing adoption of Artificial Intelligence (AI). The exponential growth of AI-driven phishing URL attacks presents new challenges for cyberspace security. This study aims to develop a novel approach, named AntiPhishX, to detect phishing and AI-phishing URL attacks effectively. The model leverages advancements in AI and service-oriented computing to enhance detection accuracy and overcome the limitations of existing methods. The proposed AntiPhishX approach integrates Natural Language Processing (NLP) techniques to extract relevant features and analyze text dependencies within URLs. A cohesive model is designed by applying machine learning (ML) algorithms to the processed feature sets. A voting-based ensemble of best-performing ML models is constructed to classify URLs as phishing, AI-phishing, or benign in real time. The model is implemented and evaluated in Python using a dataset of 90,000 URLs collected from the PhishTank platform. The AntiPhishX model outperformed benchmark models, achieving: Precision: 98.32 %, Recall: 97.63 %, F-score: 98.31 %, and Detection rate: 98.12 % The findings demonstrate the potential of AI-driven and service-oriented computing approaches, such as AntiPhishX, in strengthening cyberspace defenses against evolving phishing threats. This study highlights the effectiveness of integrating NLP and ML techniques in phishing URL detection systems. [ABSTRACT FROM AUTHOR]

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