Phishing Attack Detection through Advanced Natural Language Processing Methods
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Abstract
The rapid escalation of phishing attacks poses a significant threat to cybersecurity, necessitating the development of automated and intelligent detection mechanisms. This paper introduces an advanced Natural Language Processing (NLP)-based framework for identifying phishing attempts within emails, websites, and online communications. By leveraging deep learning-driven text analysis, semantic representation, and contextual understanding, the proposed system effectively differentiates between legitimate and malicious content. Key linguistic and structural features are extracted and modeled to capture subtle phishing indicators such as deceptive intent, abnormal lexical patterns, and misleading hyperlinks. Publicly available benchmark datasets, including phishing email and URL repositories, are utilized to evaluate the framework across diverse real-world scenarios. Experimental results reveal that the proposed approach surpasses traditional machine learning and rule-based methods in terms of accuracy, precision, recall, and F1-score. Moreover, the system demonstrates near real-time detection efficiency, making it suitable for large-scale deployment in cybersecurity infrastructures. These findings highlight the robustness and scalability of the framework as a reliable defense against evolving phishing threats.
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References
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