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Security and Privacy in Communication Networks. 17th EAI International Conference, SecureComm 2021, Virtual Event, September 6–9, 2021, Proceedings, Part II

Research Article

Phishing Website Detection from URLs Using Classical Machine Learning ANN Model

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  • @INPROCEEDINGS{10.1007/978-3-030-90022-9_28,
        author={Said Salloum and Tarek Gaber and Sunil Vadera and Khaled Shaalan},
        title={Phishing Website Detection from URLs Using Classical Machine Learning ANN Model},
        proceedings={Security and Privacy in Communication Networks. 17th EAI International Conference, SecureComm 2021, Virtual Event, September 6--9, 2021, Proceedings, Part II},
        proceedings_a={SECURECOMM PART 2},
        year={2021},
        month={11},
        keywords={Fraud protection Cybersecurity Machine learning Phishing Detection URL},
        doi={10.1007/978-3-030-90022-9_28}
    }
    
  • Said Salloum
    Tarek Gaber
    Sunil Vadera
    Khaled Shaalan
    Year: 2021
    Phishing Website Detection from URLs Using Classical Machine Learning ANN Model
    SECURECOMM PART 2
    Springer
    DOI: 10.1007/978-3-030-90022-9_28
Said Salloum1,*, Tarek Gaber1, Sunil Vadera1, Khaled Shaalan2
  • 1: School of Science, Engineering, and Environment
  • 2: Faculty of Engineering and IT
*Contact email: S.A.S.Salloum@edu.salford.ac.uk

Abstract

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.

Keywords
Fraud protection Cybersecurity Machine learning Phishing Detection URL
Published
2021-11-04
Appears in
SpringerLink
http://dx.doi.org/10.1007/978-3-030-90022-9_28
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