Development of Generative Adversarial Network-Based Algorithms for Real-Time Phishing URL Detection in Adversarial Cyber Threat Scenarios
Keywords:
Phishing Detection, Generative Adversarial Networks (GANs), Adversarial Machine Learning, Cybersecurity, URL Classification, Real-Time Threat DetectionAbstract
Phishing attacks continue to be a major cybersecurity threat, especially as adversaries increasingly deploy obfuscation and evasion strategies. Traditional detection methods—rule-based systems or standard machine learning classifiers—struggle to keep pace with the dynamic nature of phishing URLs. This study proposes a novel Generative Adversarial Network (GAN)-based architecture to detect phishing URLs in real-time, specifically under adversarial conditions. The model leverages a generator to simulate sophisticated phishing URLs and a discriminator trained to detect them. Evaluation across benchmark phishing datasets and adversarially generated URLs demonstrates that the proposed GAN-based model significantly outperforms baseline machine learning techniques in both precision and recall, offering robustness against evasion attempts. This work represents a pivotal advancement in automated, adversarially-resilient cyber threat mitigation strategies.
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Copyright (c) 2025 Jamila Maryam Zahra (Author)

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