Proceedings ArticleDOI
A novel algorithm to detect phishing URLs
Varsharani Hawanna,Vrushali Kulkarni,R. A. Rane +2 more
- pp 548-552
TLDR
A novel algorithm is proposed which will detect whether a given http URL is of phishing site or not and displays alert message if the URL found as possible phishing, otherwise it displays safe message.Abstract:
Recently, many online attacks have increased due to the growing use of the Internet, Among these the most well-known attack is phishing. Phishing is a demonstration of getting delicate data by persuading the users to uncover their own data by pretending as a trusty source in the web transaction. Most phishing attacks work by sending a forged email that contains a URL which leads you to the fake website by clicking on it. In most of the cases, phisher chooses http URLs for the attack. Along these lines, we are proposing a novel algorithm which will detect whether a given http URL is of phishing site or not. Our algorithm performs Google's updated blacklist check, utilizes Google search engine results, Alexa Ranking and no of URL-based features, for detecting phishing URLs. It displays alert message if the URL found as possible phishing, otherwise it displays safe message. This algorithm enhances the performance when dealing with known/old phishing URLs.read more
Citations
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Journal ArticleDOI
Intelligent phishing detection scheme using deep learning algorithms
TL;DR: This study focused on the design and development of a deep learning-based phishing detection solution that leveraged the universal resource locator and website content such as images, text and frames and built a hybrid classification model named the Intelligent Phishing Detection System.
Proceedings ArticleDOI
A Review of Human- and Computer-Facing URL Phishing Features
TL;DR: A review of URL-based phishing features that appear in publications targeting human-facing and automated anti-phishing approaches and focuses on both humans and computers to obtain a more comprehensive feature list and create a cross-community foundation for future research.
Proceedings ArticleDOI
A Machine Learning Approach for URL Based Web Phishing Using Fuzzy Logic as Classifier
TL;DR: This paper designs a framework of phishing detection using URL, which aims to provide a simple and efficient way of detecting phishing sites using URL.
Journal ArticleDOI
Development of anti-phishing browser based on random forest and rule of extraction framework
TL;DR: A novel technique to identify phishing websites effortlessly on the client side by proposing a novel browser architecture named as ‘Embedded Phishing Detection Browser’ (EPDB), which is a novel method to preserve the existing user experience while improving the security.
Proceedings ArticleDOI
Detecting Phishing Websites using Data Mining
TL;DR: A system which will detect old as well as newly generated phishing URLs that have completely no past behaviours to judge upon, using Data Mining is proposed.
References
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Proceedings ArticleDOI
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TL;DR: A layered anti-phishing solution that aims at exploiting the expressiveness of a rich set of features with machine learning to achieve a high true positive rate (TP) on novel phish, and limiting the FP to a low level via filtering algorithms.
Proceedings ArticleDOI
A PageRank based detection technique for phishing web sites
TL;DR: This paper aims to design and implement a new technique to detect phishing web sites using Google's PageRank, which uses the PageRank value and other features to classify phishing sites from normal sites.
Proceedings ArticleDOI
Heuristic-based Approach for Phishing Site Detection Using URL Features
TL;DR: A heuristic-based phishing detection technique that uses uniform resource locator (URL) features that phishing site URLs contain and can detect more than 98.23% of phishing sites.