Tackling the Emerging Threats of Event themed Cyberattacks: COVID-19 as Case Study
Tackling the Emerging Threats of Event themed Cyberattacks: COVID-19 as Case Study
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This project led us to answer what types of cyber abuses we have seen during the early COVID-19 era, understand website-based attacks and what types of infrastructure are leveraged by the attackers on the web, detection of those malicious websites. Finally, a team of interdisciplinary researchers worked towards tackling an ever-growing problem of misinformation propagation in social media on COVID-19 health-related information such as vaccination, masks usage, taking random medicines, and so on. Our analysis shows Random Forest is an effective model to classify malicious COVID-19 themed websites from benign websites. We also proposed an NLP-driven misinformation detection approach for generating alerts against critical health tweets.
This project led us to answer what types of cyber abuses we have seen during the early COVID-19 era, understand website-based attacks and what types of infrastructure are leveraged by the attackers on the web, detection of those malicious websites. Finally, a team of interdisciplinary researchers worked towards tackling an ever-growing problem of misinformation propagation in social media on COVID-19 health-related information such as vaccination, masks usage, taking random medicines, and so on. Our analysis shows Random Forest is an effective model to classify malicious COVID-19 themed websites from benign websites. We also proposed an NLP-driven misinformation detection approach for generating alerts against critical health tweets.
Figure: Systematizing COVID-19 attacks (red), attack techniques (blue), and attack goals (green)
Figure: Systematizing the Cyber Kill Chains for COVID-19 themed cyberattacks, which are coded in colors (see Legend).
Figure: Methodology to detect COVID-19 themed malicious websites
Figure: Top 10 abused WHOIS registrars of COVID-19 themed malicious websites (y-axis in log-scale).
Figure: Top 10 abused TLDs of COVID-19 themed malicious websites (y-axis in log-scale)
Figure: Trends of COVID-19 themed malicious website (for 2 datasets)
Remarks:
Remarks:
I have been the lead on this project in multiple directions to tackle COVID-19 themed attacks and abuses. Dr. Xu advised me on the project with his expertise in cybersecurity research for validating research questions.
I have been the lead on this project in multiple directions to tackle COVID-19 themed attacks and abuses. Dr. Xu advised me on the project with his expertise in cybersecurity research for validating research questions.
Publications:
Publications:
Mir Mehedi A. Pritom, K. M. Schweitzer, R. M. Bateman, M. Xu and S. Xu, "Data-Driven Characterization and Detection of COVID-19 Themed Malicious Websites," 2020 IEEE International Conference on Intelligence and Security Informatics (ISI), Arlington, VA, 2020, pp. 1-6, doi: 10.1109/ISI49825.2020.9280522
Mir Mehedi A. Pritom, K. M. Schweitzer, R. M. Bateman, M. Xu and S. Xu, "Characterizing the Landscape of COVID-19 Themed Cyberattacks and Defenses," 2020 IEEE International Conference on Intelligence and Security Informatics (ISI), Arlington, VA, 2020, pp. 1-6, doi: 10.1109/ISI49825.2020.9280539.
Mir Mehedi A. Pritom, Rosana Montanez Rodriguez, Asad Ali Khan, Sebastian A. Nugroho, Esra'a Alrashydah, Beatrice N. Ruiz, and Anthony Rios, "Case Study on Detecting COVID-19 Health-Related Misinformation in Social Media.'', arXiv preprint 2021, https://arxiv.org/abs/2106.06811
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