Jul 2026· International Journal of Advanced Research in Science, Communication and Technology· 0 citations· 10 references
TL;DR
Experimental evaluation demonstrates that the Hybrid CNN–LSTM model effectively classifies webpages as Safe, Suspicious, or Malicious, providing improved detection accuracy and faster prediction compared with conventional machine learning approaches.
Abstract
The rapid growth of internet services has significantly increased the number of malicious webpages that target users through phishing, malware distribution, fake login pages, and other cyberattacks. Conventional detection techniques such as blacklist-based and signature-based methods are effective only for identifying known malicious websites and often fail to detect newly created or obfuscated URLs. To overcome these limitations, this paper presents a Real-Time Malicious Webpage Detection System using a Hybrid Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) model. The proposed system automatically learns structural and sequential characteristics from URL strings without relying on manually engineered features. URL preprocessing techniques including cleaning, tokenization, encoding, and normalization are performed before training the model. The CNN component extracts lexical and structural patterns from URLs, while the LSTM component captures sequential dependencies for improved classification performance. A Flask-based web application has been developed to provide real-time URL prediction through a user-friendly interface. In addition, a Google Chrome Extension has been implemented to allow users to verify webpage safety directly while browsing. Experimental evaluation demonstrates that the Hybrid CNN–LSTM model effectively classifies webpages as Safe, Suspicious, or Malicious, providing improved detection accuracy and faster prediction compared with conventional machine learning approaches. The proposed system offers a lightweight, scalable, and practical solution for enhancing web security against evolving cyber threats
A hierarchical binary classification framework that decomposes the multiclass task into a sequence of structured binary decisions and provides competitive and feature-efficient classification performance is proposed.
Mehmet Batuhan Ozdas, Murat Osmanoglu· Journal of Computer Virology...· 0 citations
Abstract— Phishing website detection using machine learning focuses on the design and implementation of an intelligent system for detecting malicious URLs using machine learning techniques. The system aims to classify URLs as either legitimate or malicious by analyzing various structural and statistical features extracted from the URLs. A dataset containing both benign and malicious URLs is used to train and evaluate the model. The proposed approach utilizes a Gradient Boosting Classifier due to its high accuracy and ability to handle complex patterns in data. Feature extraction plays a crucial role in the project, where attributes such as URL length, presence of special characters, domain age, use of HTTPS, and abnormal patterns are considered. These features are fed into the model, which learns to differentiate between safe and harmful URLs. The project involves several stages, including data collection, preprocessing, feature extraction, model training, and performance evaluation. Multiple machine learning algorithms such as Support Vector Machine (SVM), Decision Tree, Random Forest, and XG Boost are also explored and compared to identify the most effective model .The system is designed to work in real time, allowing users to input URLs and receive instant predictions regarding their safety.
Keywords— Phishing Website Detection, Machine Learning, Gradient Boosting Classifier, URL Feature Extraction, Cybersecurity, Malicious URL Detection, Web Security, Classification, Feature Engineering, Real-Time Detection.
V. B, K. Subba Rao, G. Prasad· International Scientific Jou...· 0 citations
The findings indicate that combining deep representation learning with ensemble classification can improve dynamic malware detection and reduce dependence on static signatures.
Karthick Ganapathy· Journal of Computer and Fore...· 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
Phishing incidents remain highly frequent and destructive cyber threats affecting private users and organizations globally. Malicious actors continuously refine their deceptive strategies to evade traditional rule-based filters and conventional machine learning models, leading to financial losses, credential theft, and data breaches. This study presents an enhanced phishing email detection system based on a hybrid deep learning framework integrating a Convolutional Neural Network (CNN) with a Multi-Layer Perceptron (MLP). The CNN component automatically extracts discriminative textual features from email content, while the MLP component performs accurate email classification as legitimate or phishing. A publicly available phishing email dataset from the Kaggle platform was utilized for model training and evaluation. The dataset underwent preprocessing stages including data cleaning, text normalization, tokenization, sequence padding, and label encoding. The CNN layer extracted relevant textual patterns and semantic representations, while the MLP network performed final classification. The developed system was evaluated using accuracy, precision, recall, and F1-score. Experimental results demonstrated that the CNN–MLP model achieved 98.5% accuracy, 98% precision, 99% recall, and 99% F1-score, indicating strong capability in distinguishing phishing from legitimate emails. Additionally, a web-based application was developed to facilitate real-time phishing detection using direct text input and PDF document analysis through Optical Character Recognition (OCR). The findings demonstrate that integrating CNN-based feature extraction with MLP-based classification provides an effective, reliable, and scalable solution for phishing email detection, contributing to improved cybersecurity protection against evolving phishing attacks.
M. Yuguda, Kamil K. Saka, Y. H. Usman· FUDMA Journal of Sciences· 0 citations
The internet has rapidly evolved in its communication, commerce and information sharing making it a huge platform for cyber threats, particularly malicious URLs. They pose a serious threat to individuals and to organisations. Phishing attacks, malware distribution and other types of cybercrime frequently are carried out through malicious URLs. In this research, we have created and tested the machine learning models to detect malicious URLs. The labeled URLs used were obtained from a public dataset with more than 651,000 labeled URLs. The dataset was prepared for classification by applying data pre-processing techniques like stratified sampling, label encoding and Term Frequency–Inverse Document Frequency (TF-IDF) vectorization. To train and test the algorithms, five machine learning were used: Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Naïve Bayes (NB), Random Forest (RF) and Extreme Gradient Boosting (XGBoost), which were trained and evaluated by the metrics of accuracy, precision, recall and F1 score. The results indicated that the Random Forest model had the highest classification accuracy (95%) as compared to the other models. Moreover, a web based malicious URL detection system was developed to demonstrate the actual application of the developed models in real time cyber security scenarios. The study provides a conclusion that the machine learning techniques, particularly ensemble learning techniques can be considered an effective and reliable technique to detect malicious URL.
Oludele Adeleke, Jimoh Abdulhakeem Kuranga, Samuel Adeolu Ogunbiyi et al.· American Journal of Interdis...· 0 citations
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