Location
Virginia Modeling, Analysis and Simulation Center, Room 1201
Conference Title
Modeling, Simulation and Visualization Student Capstone Conference 2023
Conference Track
Data Science
Document Type
Paper
Abstract
In today's business landscape, cyberattacks present a significant threat that can lead to severe financial losses and damage to a company's reputation. To mitigate this risk, it is essential for stakeholders to have an understanding of the latest types and patterns of cyberattacks. The primary objective of this research is to provide this knowledge by utilizing the Advisen cyber loss dataset, which comprises over 137,000 cyber incidents that occurred across various industry sectors from 2013 to 2020. By using text mining techniques, this paper will conduct an exploratory data analysis to identify the most common types of malware, including ransomware. Furthermore, the study will include a likelihood and severity analysis to evaluate the financial impact of these cyberattacks on businesses. Ultimately, this study aims to shed light on the prevalence and financial repercussions of malware incidents and provide businesses with valuable insights to help develop effective cybersecurity strategies.
Keywords:
Ransomware, Malware, Cyber-attack, Advisen data loss, Likelihood, Severity
Start Date
4-20-2023
End Date
4-20-2023
Recommended Citation
Abdelmagid, Ahmed M.; Javadnejad, Farshid; Pinto, C. Ariel; McShane, Michael K.; Diaz, Rafael; and Gartell, Elijah, "Assessing the Frequency and Severity of Malware Attacks: An Exploratory Analysis of the Advisen Cyber Loss Dataset" (2023). Modeling, Simulation and Visualization Student Capstone Conference. 2.
https://digitalcommons.odu.edu/msvcapstone/2023/datascience/2
DOI
10.25776/rkd6-1d77
Included in
Assessing the Frequency and Severity of Malware Attacks: An Exploratory Analysis of the Advisen Cyber Loss Dataset
Virginia Modeling, Analysis and Simulation Center, Room 1201
In today's business landscape, cyberattacks present a significant threat that can lead to severe financial losses and damage to a company's reputation. To mitigate this risk, it is essential for stakeholders to have an understanding of the latest types and patterns of cyberattacks. The primary objective of this research is to provide this knowledge by utilizing the Advisen cyber loss dataset, which comprises over 137,000 cyber incidents that occurred across various industry sectors from 2013 to 2020. By using text mining techniques, this paper will conduct an exploratory data analysis to identify the most common types of malware, including ransomware. Furthermore, the study will include a likelihood and severity analysis to evaluate the financial impact of these cyberattacks on businesses. Ultimately, this study aims to shed light on the prevalence and financial repercussions of malware incidents and provide businesses with valuable insights to help develop effective cybersecurity strategies.