Countering Malicious URLs in Internet of Things Using a Knowledge-Based Approach and a Simulated Expert

Sajid Anwar, Feras Al-Obeidat, Abdallah Tubaishat, Sadia Din, Awais Ahmad*, Fakhri Alam Khan, Gwanggil Jeon, Jonathan Loo

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

15 Scopus citations


This article proposes a novel methodology to detect malicious uniform resource locators (URLs) using simulated expert (SE) and knowledge-base system (KBS). The proposed study not only efficiently detects known malicious URLs but also adapts countermeasure against the newly generated malicious URLs. Moreover, this article also explored which lexical features are contributing more in final decision using a factor analysis method, and thus help in avoiding the involvement of human experts. Furthermore, we apply the following state-of-the-art machine learning (ML) algorithms, i.e., naïve Bayes (NB), decision tree (DT), gradient boosted trees (GBT), generalized linear model (GLM), logistic regression (LR), deep learning (DL), and random rest (RF), and evaluate the performance of these algorithms on a large-scale real data set of data-driven Web applications. The experimental results clearly demonstrate the efficiency of NB in the proposed model as NB outperforms when compared to the rest of the aforementioned algorithms in terms of average minimum execution time (i.e., 3 s) and is able to accurately classify the 107 586 URLs with 0.2% error rate and 99.8% accuracy rate.

Original languageEnglish
Article number8908778
Pages (from-to)4497-4504
Number of pages8
JournalIEEE Internet of Things Journal
Issue number5
StatePublished - May 2020
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2014 IEEE.


  • Feature extraction
  • URL classification
  • malicious URLs
  • naïve Bayes (NB)
  • simulated experts (SEs)

ASJC Scopus subject areas

  • Signal Processing
  • Information Systems
  • Hardware and Architecture
  • Computer Science Applications
  • Computer Networks and Communications


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