SeizeMaliciousURL: A novel learning approach to detect malicious URLs

Dipankar Kumar Mondal, Bikash Chandra Singh, Haibo Hu, Shivazi Biswas, Zulfikar Alom, Mohammad Abdul Azim

Research output: Journal article publicationJournal articleAcademic researchpeer-review

3 Citations (Scopus)

Abstract

Malicious websites are increasing in abundance, which brings serious web security threats to users. As a result, individuals lose their assets, values, information, etc. to unauthorized parties and become victims while visiting such websites. The research community put efforts into developing effective and efficient models for detecting malicious URLs to make available notifications about websites that users access. Perceiving this, numerous methods make use of various types of machine learning (ML) approaches. However, until now, no technique has perfectly detected malicious URLs, as they are susceptible to false positive and false negative decisions in classifying URLs in malicious and non-malicious groups. To improve this issue, this article proposes a new approach based on a machine learning technique. The proposed approach uses multiple classifiers (i.e., ensemble learning) to predict the class probabilities of URLs and then applies a threshold to filter the decisions of multiple classifiers. Next, the decisions are combined concerning its corresponding class probabilities and find the class label with the highest class probability as the final decision on unlabeled URLs. The results show that the proposed method offers better performance in terms of malicious URL detection than other methods.

Original languageEnglish
Article number102967
Pages (from-to)1-10
Number of pages10
JournalJournal of Information Security and Applications
Volume62
DOIs
Publication statusPublished - Nov 2021

Keywords

  • Classification
  • Machine learning
  • Malicious URLs detection

ASJC Scopus subject areas

  • Software
  • Safety, Risk, Reliability and Quality
  • Computer Networks and Communications

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