Jul 2026· 2026 International Conference on Intelligent and Sustainable AI Systems (ICOSAAS)· pp. 662-670· 0 citations· 24 references
Abstract
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.
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
Phishing websites continue to pose a serious cybersecurity threat by deceiving users into revealing sensitive information such as login credentials, banking details, and personal data. Traditional blacklist-based detection techniques are ineffective against newly created phishing websites, necessitating intelligent machine learning solutions. This paper presents PhishShield, a hybrid phishing website detection framework that integrates Support Vector Machine (SVM) and Light Gradient Boosting Machine (LightGBM) to accurately classify legitimate and phishing websites. The proposed approach utilizes URL-based feature extraction and text preprocessing to generate meaningful representations for classification. SVM provides robust decision boundaries, while LightGBM enhances predictive performance through efficient gradient boosting. Experimental evaluation demonstrates that the hybrid framework achieves higher accuracy, precision, recall, and F1-score compared to conventional machine learning models. The system is implemented as a web-based application capable of real-time URL analysis, enabling users to identify malicious websites before accessing them. The proposed framework offers an efficient, scalable, and reliable solution for strengthening web security against evolving phishing attacks.
Srija Pasupunuti, Sk.Mahammadunnisa· American Journal of AI Cyber...· 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
The model proposed employs feature extraction using URLs, such as lexical and structural features like URL length, frequency of special characters, use of IP addresses, and occurrence of suspicious keywords, to improve the accuracy and reliability of detection.
Muna Rashid Hameed· Iraqi Journal for Computers...· 0 citations
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.
Research Paper, Wong Ki Hurn, T. Yan et al.· International Journal of Eme...· 0 citations
A hybrid real-time phishing detection system in the form of a Google Chrome extension that uses a trusted domain whitelist for false positives on legitimate banking and government websites and a pattern-based blocklist for piracy and malware domains.
Kavila Moni Sushma Deep, Pavan KumarSeepana, Natasha Rayi et al.· International journal of re...· 0 citations
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