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Characterizing IoC of Covid-19 Spam Campaign by Open-Source based Geographic Analysis
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 186 - Number 11 |
| Year of Publication: 2024 |
| Authors: Ruo Ando, Liu Shiying, Yuki Okawa, Yoshiyasu Takefuji |
10.5120/ijca2024923449
|
Ruo Ando, Liu Shiying, Yuki Okawa, Yoshiyasu Takefuji . Characterizing IoC of Covid-19 Spam Campaign by Open-Source based Geographic Analysis. International Journal of Computer Applications. 186, 11 ( Mar 2024), 12-16. DOI=10.5120/ijca2024923449
Abstract
The use of geographic analysis in the field of cybersecurity is growing. However, few studies have evaluated implementation methods and algorithms. In this paper, we characterize each of the IoCs (Indicators of Compromise) by comparing the open-source Reported Blocklist Database (AbuseIPDB) and the IoCs of the Covid-19 Spam campaign based on VirusTotal scores. VirusTotal scores range from 40 to 100, with 40 points being used for widespread and less certain threat-hunting rules and 100 points being used for the most certain rules. The experiments revealed that OPTICS, a non-parametric, density-based method, is effective due to the nature of the geographic distribution of cybersecurity IoCs. It was also found that although the danger scores of both IoCs were close, the IoCs of the Covid-19 Spam campaign contained more dangerous ones and required more alerts. The proposed methodology applies to other types of IoCs, all of which can be implemented with open source resources and APIs on the Internet.
References
- Indicators of Compromise (IoCs) and Their Role in Attack Defence https://www.cisa.gov/news-events/cybersecurity-advisories/aa20-099a
- COVID-19 Exploited by Malicious Cyber Actors https://www.cisa.gov/news-events/cybersecurityadvisories/aa20-099a
- AbuseIPDB https://www.abuseipdb.com/
- VirusTotal https://www.virustotal.com/gui/
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