Investigating whether HTML structure can serve as a robust fingerprint for identifying phishing template reuse demonstrates that structural representations of webpages can effectively reveal hidden similarities across phishing sites, enabling the detection of emerging and zero-day templates and supporting the analysis of coordinated phishing threats.
Abstract
Phishing remains a persistent and evolving cybersecurity threat, with attack volumes reaching record levels. This growth is driven by the industrialization of phishing through widely available phishing kits and reusable templates, which enable cybercriminals to rapidly generate and deploy large numbers of fraudulent webpages. Although surface-level attributes may differ across these websites, their underlying structures often exhibit significant similarities. However, most existing defenses rely on reactive blocklists or supervised classification models that focus on individual phishing instances, limiting their ability to identify structural reuse and detect coordinated phishing campaigns. To address this limitation, this study investigates whether HTML structure can serve as a robust fingerprint for identifying phishing template reuse. We model webpages as Document Object Model (DOM) trees and extract structural features, optionally enriched with HTML tag-based content information. These representations are then clustered using unsupervised learning methods to group structurally similar webpages. Three clustering algorithms are evaluated and compared, while also analyzing how the depth of the extracted DOM-tree affects cluster formation and overall clustering performance. Finally, cluster quality is also evaluated both quantitatively and qualitatively, including a novel level-wise Jaccard Distance Score and manual inspection supported by visualization tools. Results demonstrate that structural representations of webpages can effectively reveal hidden similarities across phishing sites, enabling the detection of emerging and zero-day templates and supporting the analysis of coordinated phishing threats
Efficiency makes the proposed approach exceptionally suitable for real-time detection in resource-constrained environments, such as mobile applications or browser extensions, providing an accessible and proactive layer of defense for end-users.
Phishing websites are short-lived and rapidly changing, yet many phishing datasets reduce observations to URLs or precomputed features, constraining researchers to predefined representations and discarding the underlying evidence needed to derive alternative features, apply new extraction methods, examine cross-layer relationships, and reanalyze observations as phishing techniques evolve. This study addresses this limitation with a multi-layer active-web dataset comprising 67,502 scans, including 33,387 phishing observations from operational feeds and 34,115 screened benign reference observations. The corpus preserves raw evidence across HTML content and screenshots, URL and redirect behavior, HTTP and security headers, compliance files, TLS certificates, DNS and domain registration, open ports, geolocation and accessibility measurements, and network infrastructure, while explicitly recording unavailable evidence rather than treating it as negative observations. To avoid misleading infrastructure attribution on shared platforms, the study applies a hosting-aware evidence model that masks provider-owned infrastructure signals for free-hosted tenant pages while retaining meaningful page- and transport-level evidence. Characterization reveals systematic differences between phishing and benign websites across web-resource usage, domain maturity, mail and policy configuration, security headers, and infrastructure context. By preserving raw artifacts together with acquisition metadata and explicit evidence availability, the corpus provides an inspectable and reproducible foundation for future phishing measurement and dataset research.
Furkan Çolhak, Ferhat Demirkıran, Hasan Dağ et al.· 0 citations
A hybrid browser-resident phishing detection framework that combines three complementary detection mechanisms: a locally executed Random Forest model using URL lexical features, lightweight real-time DOM structure analysis, and VirusTotal’s multi-engine reputation service is proposed.
Muhammad Arshad, Beena Sherin Kuriakose, C. W. Onn et al.· Frontiers of Computer Scienc...· 0 citations
A novel, hybrid detection framework that constructs a structured “portrait” of each URL by extracting 12 discriminative features-spanning structural, semantic, technical, and morphological dimensions, addressing critical gaps in existing black-box AI systems and aligns with the stringent transparency requirements of security-critical domains such as banking, e-government, and enterprise cybersecurity.
S. Buchyk, S. Toliupa, Anastasiia Shabanova et al.· CSDP· 0 citations
In the technology era, Phishing has continued to be a great challenge within the cybersecurity and web security landscape. This involves exploiting human trust on any online services and subtle technical flaws. This is to gather credentials, financial data, and sensitive information across diverse online platforms and various users. Traditional defenses like static blacklists, signature-based filters and simple detection rules are limited by slow update cycles and an inability to capture subtle syntactic and behavioral cues. To address these shortcomings, we propose a hybrid detection framework that fuses classical supervised machine-learning classifiers (e.g., Logistic Regression, SVM, Random Forest, XGBoost) with sequence-aware deep learning (LSTM) to jointly model lexical, structural, syntactic, and behavioral features extracted from URLs and webpage metadata. This combined approach leverages the interpretability and stability of ML models alongside the pattern-learning strength of LSTMs to detect both known and zero-day phishing attempts, produce calibrated confidence scores and deliver comprehensive reports via a real-time web interface resulting in a robust, transparent, and operationally useful solution for strengthening web security.
M. Yaswanth, Pathan Basheer Khan, Dhulipalla Naga Harish et al.· 2026 7th International Confe...· 0 citations
External validation against ten official Iraqi university websites resulted in correct classification with no false positives, demonstrating that the proposed phishing detection model is reliable.
