Phishing website detection from URLs using classical machine learning ANN model

Salloum, S ORCID:, Gaber, T ORCID:, Vadera, S ORCID: and Shaalan, K ORCID: 2021, Phishing website detection from URLs using classical machine learning ANN model , in: International Conference on Security and Privacy in Communication Systems, SECURECOMM 2021, 6th-9th September 2021, Online.

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Phishing is a serious form of online fraud made up of spoofed websites that attempt to gain users’ sensitive information by tricking them into believing that they are visiting a legitimate site. Phishing attacks can be detected many ways, including a user's awareness of fraud protection, blacklisting websites, analyzing the suspected characteristics, or comparing them to recent attempts that followed similar patterns. The purpose of this paper is to create classification models using features extracted from websites to study and classify phishing websites. In order to train the system, we use two datasets consisting of 58,645 and 88,647 URLs labeled as “Phishing” or “Legitimate”. A diverse range of machine learning models such as “XGBOOST, Support Vector Machine (SVM), Random Forest (RF), k-nearest neighbor (KNN), Artificial neural network (ANN), Logistic Regression (LR), Decision tree (DT), and Gaussian naïve Bayes (NB)” classifiers are evaluated. ANN provided the best performance with 97.63% accuracy for detecting phishing URLs in experiments. Such a study would be valuable to the scientific community, especially to researchers who work on phishing attack detection and prevention.

Item Type: Conference or Workshop Item (Paper)
Schools: Schools > School of Computing, Science and Engineering
Journal or Publication Title: Security and Privacy in Communication Networks : 17th EAI International Conference, SecureComm 2021, Virtual Event, September 6–9, 2021, Proceedings, Part II
Publisher: Springer
Series Name: Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
ISBN: 9783030900212 (paperback); 9783030900229 (ebook)
ISSN: 1867-8211
Related URLs:
Depositing User: USIR Admin
Date Deposited: 09 Feb 2022 09:23
Last Modified: 15 Feb 2022 14:48

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