Jul 2026· Iraqi Journal for Computers and Informatics· 0 citations· 16 references
TL;DR
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.
Abstract
Phishing attacks are considered one of the highest risks to cybersecurity, as they exploit users by pretending to be a valid site and gaining access to sensitive data. Traditional methods of detection, including blacklist-based systems, cannot uncover newly created phishing sites (zero-day attacks), need regular updates to their databases, and can generally not offer real-time protection. The paper suggests implementing a hybrid phishing site detection system involving machine learning with rule-based analysis to improve the accuracy and reliability of detection. 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. Three supervised machine learning algorithms, Support Vector Machine (SVM), Random Forest (RF), and Logistic Regression, where RF was indicated to achieve the highest performance. Moreover, a rule-based system of brand impersonation detection is also implemented to make the system resistant to targeted phishing attacks. Experimental findings prove that the hybrid methodology enhances classification accuracy and lowers false negatives compared to conventional ones. The proposed hybrid model represents an accuracy of 96%, with a recall of 94% and F1 of 95% ; it can be deployed in real-time and gives interpretable results by using confidence scoring. The solution suggested is a feasible and effective method of phishing detection and can be expanded to more sophisticated intelligent cybersecurity systems.
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 development of internet technology has increased the intensity of digital activities, but it has also been followed by an increase in cybersecurity threats, one of which is phishing attacks through malicious URLs. Phishing is a fraudulent method that is carried out by manipulating users through fake websites that resemble official websites to obtain sensitive information, such as usernames, passwords, and financial data. Conventional blacklist-based detection methods are considered less effective in recognizing new phishing URLs that continue to develop dynamically. Therefore, this study aims to analyze and compare the performance of various machine learning and deep learning algorithms in accurately detecting phishing URLs. The dataset used was obtained from Kaggle with a total of 11,054 data points and 31 features that represent the characteristics of phishing and legitimate URLs. The methods used include Logistic Regression, K-Nearest Neighbor, Support Vector Machine, Naive Bayes, Decision Tree, Random Forest, Gradient Boosting, CatBoost, Extreme Gradient Boosting, and Multilayer Perceptron. The research stages include data preprocessing, exploratory data analysis, data visualization, separation of training and testing data, model training, and performance evaluation using accuracy, precision, recall, and F1-score. The results showed that the Gradient Boosting algorithm provided the best performance with an accuracy of 0.974, an F1-score of 0.977, a recall of 0.994, and a precision of 0.986. The results show that the ensemble learning method is able to detect phishing URLs effectively and can be used to improve artificial intelligence-based cybersecurity systems.
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
The high rate of internet service expansion has posed a great challenge due to a high number of malicious sites that conduct internet activities that include phishing, malware downloads and online fraud. These threats must be detected at the initial stage and improve security to make people resistant to possible threats. This paper introduces a machine learning-based system to identify malicious websites based on the URL-based features. The suggested method is aimed at deriving both lexical and structural attributes of URLs, such as URL length, the use of special characters, subdomains and protocols. They are the characteristics based on which classification models that can differentiate between benign and malicious URLs are trained. A smart detection system, which is known as the Adaptive URL Feature Intelligence Algorithm (AUFIA), is proposed to enhance analyzing features with the use of feature weighting and normalization algorithms. The experimental assessment proves that the given approach has high detection accuracy and better classification performance than some of the existing methods. The results suggest that feature engineering and machine learning methods are effective in offering scalable and effective solutions to the problem of malicious websites.
Sampath Mnv, Nahita Pathania· 2026 4th International Confe...· 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
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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