Jul 2026· 2026 7th International Conference on Smart Systems and Inventive Technology (ICSSIT)· pp. 1994-1999· 0 citations· 15 references
Abstract
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.
A hybrid phishing detection framework that integrates three complementary techniques: DistilBERT for semantic analysis of URL text, Graph Neural Networks for modelling structural relationships among URL components, and LightGBM for efficient metadata-based feature classification is proposed.
I. Shalini, G. Sujini· International Journal for Re...· 0 citations
Phishing attacks remain a prevalent and rapidly evolving cybersecurity threat, leveraging deceptive Uniform Resource
Locators (URLs) and fraudulent websites to steal sensitive user data, financial credentials, and personal information.
Traditional detection mechanisms, such as blacklist-based and heuristic approaches, struggle to mitigate zero-day phishing
threats due to their reliance on static, manually updated databases. While machine learning and ensemble techniques have
enhanced detection accuracy, existing literature predominantly focuses on offline evaluations using static datasets, offering
limited support for real-time deployment, adaptability to evolving attack patterns, and continuous monitoring. To bridge this gap,
this paper introduces a hybrid machine learning framework for real-time phishing website detection. The proposed architecture
integrates an Artificial Neural Network (ANN) and a Bagging K-Nearest Neighbors (Bagging-KNN) classifier through a
Logistic Regression-based stacking ensemble, combining their complementary learning capabilities to maximize classification
performance while minimizing prediction error. Developed using the PhiUSIIL Phishing URL Dataset, the framework
implements a leakage-free machine learning pipeline encompassing automated data cleaning, a train/test split performed prior
to any preprocessing, feature engineering, StandardScaler-based normalization, SMOTE-based class balancing, and
SelectKBest feature selection, all splitting-dependent steps fitted exclusively on the training partition. The framework’s efficacy
is validated using Accuracy, Precision, Recall, F1-score, ROC-AUC, and Confusion Matrix analysis. Beyond offline validation,
the model is operationalized through two real-time deployment channels: a Gradio-based web interface for on-demand URL
analysis, and a Chrome browser extension that automatically screens the active browser tab using a combination of rule-based
checks, live queries to the deployed model, and a local heuristic fallback. By unifying ensemble learning, a leakage-conscious
preprocessing pipeline, and dual real-time deployment tools, the proposed framework provides an effective, transparently
evaluated solution for real-world phishing detection, while explicitly discussing the boundaries within which its strongest offline
results should be interpreted.
E. Kavya, A. S. Chakravarthy· International Journal for Re...· 0 citations
A feature-driven framework for phishing Uniform Resource Locator (URL) detection is introduced, emphasizing the design and evaluation of enhanced feature representations and highlighting that performance gains are primarily driven by feature design rather than model complexity.
Deniz Kaya, Murat Osmanoğlu· PeerJ Computer Science· 0 citations
A Phishing Website Detection System Using a Stacked Hybrid Model With Explainable AI, designed to accurately classify websites as phishing or legitimate while providing transparent decision explanations, is presented.
A. Mounika, D. Ramakrishna· International Journal for Re...· 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.
Phishing websites still pose a threat to internet users by using well-known domain names and confusing URL formats to trick them into divulging confidential information. This research proposes a phishing website detection system and a cyberattack prevention system based on a deep learning model designed to detect whether raw URLs are phishing or legitimate, leveraging a transformer model. The system analyzes URL sequences to identify patterns, domain name irregularities, suspicious tokens, unusual lengths, special character usage, and deceptive subdomains. A web interface enables the user to input URLs, and the Flask backend performs pre-processing, tokenization, model inference, and suspicious feature identification. The proposed method fuses the transformer-based semantic representation of the URL and lexical feature analysis to reliably detect phishing and provide explainable warning features. The system creates a prediction label, confidence score, and suspicious features in real time to support the decision-making process. This work provides a practical and scalable solution for phishing identification, user protection, and web-based cyberattack prevention.
Dr. N. Ramadevi, Dr. K. Uday, Kumar et al.· 2026 International Conferenc...· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
