Phikita Phishing Kit Attacks Dataset for Phishing Websites Identification Felipe
DOI:
https://doi.org/10.69996/fmep.2025024Keywords:
Phishing Detection, Phishing Kits, Website Classification, Machine Learning, PhiKitA, CybersecurityAbstract
Phishing attacks continue to evolve in sophistication and scale, with attackers increasingly relying on phishing kits to streamline the deployment of malicious websites. These kits enable the rapid and widespread execution of phishing campaigns, posing significant challenges to cybersecurity systems. Despite the growing use of phishing kits, there exists a critical gap in publicly available datasets that directly associate deployed phishing websites with the underlying kits used. To address this, we introduce PhiKitA, a novel dataset comprising both phishing kits and the corresponding phishing websites generated using them. Our study conducts three core experiments utilizing MD5 hashing, fingerprinting, and graph-based Document Object Model (DOM) representation algorithms. These experiments aim to: (1) analyze the familiarity and structural patterns of phishing kit samples, (2) detect phishing websites based on kit-derived features, and (3) trace the origin of phishing sites to specific kits. The graph-based approach demonstrated superior performance in phishing detection, achieving an accuracy of 92.50%, thereby underscoring the discriminatory power of structural data. Conversely, the MD5 hashing technique yielded a modest F1-score of 39.54%, indicating limitations in capturing semantic similarities. Overall, PhiKitA provides a foundational step toward deeper understanding and early detection of phishing campaigns through kit-based analysis.
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