Segmentation-based Phishing URL Detection

Aung, Eint Sandi; Yamana, Hayato

doi:10.1145/3486622.3493983

Cited by 6 publications

(2 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A URL has two main components : protocol identifier (indicates what protocol to use) and resource name (specifies the IP address or the domain name where the resource is located) [21]. URL-based phishing attacks are mainly performed by embedding sensitive words or characters in a link that [22]:…”

Section: Phishing Attackmentioning

confidence: 99%

Combining Lexical, Host, and Content-based features for Phishing Websites detection using Machine Learning Models

Hamadouche,

Boudraa,

Gasmi

2024

ICST Transactions on Scalable Information Systems

View full text Add to dashboard Cite

In cybersecurity field, identifying and dealing with threats from malicious websites (phishing, spam, and drive-by downloads, for example) is a major concern for the community. Consequently, the need for effective detection methods has become a necessity. Recent advances in Machine Learning (ML) have renewed interest in its application to a variety of cybersecurity challenges. When it comes to detecting phishing URLs, machine learning relies on specific attributes, such as lexical, host, and content based features. The main objective of our work is to propose, implement and evaluate a solution for identifying phishing URLs based on a combination of these feature sets. This paper focuses on using a new balanced dataset, extracting useful features from it, and selecting the optimal features using different feature selection techniques to build and conduct acomparative performance evaluation of four ML models (SVM, Decision Tree, Random Forest, and XGBoost). Results showed that the XGBoost model outperformed the others models, with an accuracy of 95.70% and a false negatives rate of 1.94%.

show abstract

Section: Phishing Attackmentioning

confidence: 99%

Combining Lexical, Host, and Content-based features for Phishing Websites detection using Machine Learning Models

Hamadouche,

Boudraa,

Gasmi

2024

ICST Transactions on Scalable Information Systems

View full text Add to dashboard Cite

show abstract

“…Considering the type of attack rather than the method of detection, [13] provides an overview of recent phishing URL detection studies. The authors reviewed 13 studies between 2014 and 2019 in terms of algorithms used, performance metrics and proc and cons in the study.…”

Section: Related Workmentioning

confidence: 99%

Using a Machine Learning Model for Malicious URL Type Detection

Tung

Wong

Kuzminykh

et al. 2022

Lecture Notes in Computer Science

View full text Add to dashboard Cite

The world wide web, beyond its benefits, has also become a major platform for online criminal activities. Traditional protection methods against malicious URLs, such as blacklisting, remain a valid alternative, but cannot detect unknown sites, hence new methods are being developed for automatic detection, using machine learning approaches. This paper strengthens the existing state of the art by proposing an alternative machine learning approach, that uses a set of 14 lexical and host-based features but focuses on the typical mechanisms employed by malicious URLs. The proposed method employs random forest and decision tree as core mechanisms and is evaluated on a combined benign and malicious URL dataset, which indicates an accuracy of over 97%.

show abstract

Hybrid Phishing URL Detection Using Segmented Word Embedding

Aung

Yamana

2022

Information Integration and Web Intelligence

View full text Add to dashboard Cite

Segmentation-based Phishing URL Detection

Cited by 6 publications

References 7 publications

Combining Lexical, Host, and Content-based features for Phishing Websites detection using Machine Learning Models

Combining Lexical, Host, and Content-based features for Phishing Websites detection using Machine Learning Models

Using a Machine Learning Model for Malicious URL Type Detection

Hybrid Phishing URL Detection Using Segmented Word Embedding

Contact Info

Product

Resources

About