URLdeepDetect: A Deep Learning Approach for Detecting Malicious URLs Using Semantic Vector Models

Sara Afzal, Muhammad Asim, Abdul Rehman Javed, Mirza Omer Beg, Thar Baker

Research output: Contribution to journalArticlepeer-review

Abstract

Malicious Uniform Resource Locators (URLs) embedded in emails or Twitter posts have been used as weapons for luring susceptible Internet users into executing malicious content leading to compromised systems, scams, and a multitude of cyber-attacks. These attacks can potentially might cause damages ranging from fraud to massive data breaches resulting in huge financial losses. This paper proposes a hybrid deep-learning approach named URLdeepDetect for time-of-click URL analysis and classification to detect malicious URLs. URLdeepDetect analyzes semantic and lexical features of a URL by applying various techniques, including semantic vector models and URL encryption to determine a given URL as either malicious or benign. URLdeepDetect uses supervised and unsupervised mechanisms in the form of LSTM (Long Short-Term Memory) and k-means clustering for URL classification. URLdeepDetect achieves accuracy of 98.3% and 99.7% with LSTM and k-means clustering, respectively.

Original languageEnglish
Article number21
JournalJournal of Network and Systems Management
Volume29
Issue number3
DOIs
Publication statusPublished - 4 Mar 2021

Bibliographical note

Publisher Copyright:
© 2021, The Author(s), under exclusive licence to Springer Science+Business Media, LLC part of Springer Nature.

Keywords

  • Deep neural networks
  • Malicious URL detection
  • Security and privacy
  • Word embedding

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