Jul 2026· International Journal of Emerging Multidisciplinaries: Computer Science & Artificial Intelligence· 0 citations· 25 references
TL;DR
The multi-modal approach improves accuracy, reduces mistakes, and adapts better to new phishing methods, and performs better than single-method systems and has strong potential for future improvement.
Abstract
Phishing is one of the most common online threats, and it is becoming harder to detect because attackers use AI nowadays, fake website designs, and new tricks. Traditional methods like simple rules or URL reputation checks are no longer enough to stop these modern attacks. This report introduces Phishing Shield AI, a phishing detection system that uses three types of analysis: text analysis with NLP, URL and domain checking, and computer vision to compare webpage visuals. Each part gives a risk score, and the system combines them to decide whether something is phishing. It also provides clear explanations by showing which words, links, or images look suspicious. Based on the literature review, gap analysis, system design, and evaluation, the multi-modal approach improves accuracy, reduces mistakes, and adapts better to new phishing methods. Overall, Phishing Shield AI is a practical and scalable solution that performs better than single-method systems and has strong potential for future improvement.
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
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 attacks are one of the long-lasting problems in cybersecurity. Attackers are always changing their techniques to avoid being caught by conventional detection techniques. The techniques used currently are based on static blacklists or individual classifiers using features, but this does not completely represent the phishing attack. This project fills this critical information security gap by developing a hybrid real-time phishing detection system in the form of a Google Chrome extension. Our system uses a combination of two independent machine learning pipelines. In the first pipeline, we use thirty hand-crafted features to classify URL structure using a Gradient Boosting Classifier on 11,054 samples with 97.4% accuracy. In the second pipeline, we use raw HTML data from the webpage. We use a TF-IDF vectorizer on HTML data injected with semantic flag tokens. We use a Random Forest Classifier on 1,859 real site files with 82.80% accuracy. We combine the results using a weighted combination. We use a Retrieval Augmented Generation module, which uses a Google Gemini language model. We use a FAISS vector index to produce a risk explanation in a human-readable format. We use a trusted domain whitelist for false positives on legitimate banking and government websites. We use a pattern-based blocklist for piracy and malware domains. We use a popup interface to show users Safe or Phishing results. We use a full-screen alert overlay injected into the active tab for users. We use experimental results to show that our system performs better than any individual baseline
Kavila Moni Sushma Deep, Pavan KumarSeepana, Natasha Rayi et al.· International journal of re...· 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
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.
Ms. I. Shalini, Ms. G. Sujini· International Journal for Re...· 0 citations