Detecting malicious COVID-19 URLs using machine learning techniques

Jamil Ispahany, Rafiqul Islam

Research output: Book chapter/Published conference paperConference paperpeer-review

10 Citations (Scopus)

Abstract

Throughout the COVID-19 outbreak, malicious attacks have become more pervasive and damaging than ever. Malicious intruders have been responsible for most of the cybercrimes committed recently and are the cause for a growing number of cyber threats, including identity and IP thefts, financial crimes, and cyber-attacks to critical infrastructures. Machine learning (ML) has proven itself as a prominent field of study over the past decade due to solving highly complex and sophisticated realworld problems. This paper proposes an ML-based classification technique to detect the growing number of malicious URLs, due to the COVID-19 pandemic, which is currently considered a threat to IT users. We have used a large volume of Open Source data and preprocessed it using our developed tool to generate feature vectors and trained the ML model using an apprehensive malicious threat weight. Our ML model has been tested, with and without entropy to forecast the threatening factors of COVID-19 URLs. The empirical evidence proves our methods to be a promising mechanism to mitigate COVID-19 related threats early in the attack lifecycle.

Original languageEnglish
Title of host publication2021 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events, PerCom Workshops 2021
Place of PublicationUnited States
PublisherIEEE, Institute of Electrical and Electronics Engineers
Pages718-723
Number of pages6
ISBN (Electronic)9781665404242
ISBN (Print)9781665447249 (Print on demand)
DOIs
Publication statusPublished - 22 Mar 2021
EventPerCom 2021: The 19th International Conference on Pervasive Computing and Communications 2021 - Virtual, Germany
Duration: 22 Mar 202126 Mar 2021
https://drive.google.com/file/d/1nLj_2g3UsnjgIS-zUJXxi70K8VbkH3HO/view (SPT-IoT 2021: The Fifth Workshop on Security, Privacy and Trust in the Internet of Things (part of PerCom 2021) program)
https://web.archive.org/web/20220307181306/http://percom.uta.edu/ (Conference website)
https://ieeexplore.ieee.org/xpl/conhome/9430855/proceeding (Proceedings)

Publication series

Name2021 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events, PerCom Workshops 2021

Conference

ConferencePerCom 2021
Country/TerritoryGermany
Period22/03/2126/03/21
OtherIEEE PerCom is the premier conference for presenting scholarly research in pervasive computing and communications. Advances in this field are leading to innovative platforms, protocols, systems, and applications for always-on, always-connected services.

In 2021, PerCom will visit Kassel, situated at the geographic center of Germany and a dynamic industrial and cultural city. It is known for its UNESCO World Heritage site 'Bergpark Wilhemshöhe' and famous for a leading exhibition of contemporary art 'documenta'.

In the light of the international situation of the CoVID-19 pandemic, the PerCom General Chairs and Steering Committee have decided that PerCom 2021 will be virtual (synchronous zoom meeting).
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  • L&T Scholarship Reflection

    Islam, R. (Speaker)

    01 Jan 202231 Dec 2022

    Activity: Scholarly activities in Learning and Teaching reflectionPeer reviewed publication reflection

  • L&T Scholarship Reflection

    Islam, R. (Speaker)

    12 Jan 202110 Jan 2022

    Activity: Scholarly activities in Learning and Teaching reflectionPeer reviewed publication reflection

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