Analyzing and Estimating Cyberattack Trends by Performing Data Mining on a Cybersecurity Data Set
More than five billion personal information has been compromised over the past eight years through data breaches from notable companies, and the damage related to cybercrime is expected to reach six trillion USD annually by the year of 2021. Interestingly, recent cyberattacks were aimed specifically at credit agencies and companies that hold credit information of their customers and employees. The question is: “Why is it difficult to protect against or evade cyberattacks even for these prestigious companies?”. The purpose of this research is to bring the notion of notorious, rapidly-multiplying cyberthreats. Hence, the research focuses on analyzing cyberattack techniques and finding effectiveness of surveillance methods that companies utilize to protect themselves from cyberattacks. In order to achieve this, we selected cyberattacks information and analyzed the data set through data mining, and the research findings suggest a future trend of cyberattacks efficient countermeasures. From the information gathered through data mining, the research findings suggest a future trend of cyberattacks and efficient countermeasures.
Dr. Enping Li, Thesis Advisor
Dr. Laura Gross, Committee Member
Dr. Haleh Khojasteh, Committee Member
Copyright and Permissions
Original document was submitted as an Honors Program requirement. Copyright is held by the author.
Young Koh, Chan. (2019). Analyzing and Estimating Cyberattack Trends by Performing Data Mining on a Cybersecurity Data Set. In BSU Honors Program Theses and Projects. Item 408. Available at: https://vc.bridgew.edu/honors_proj/408
Copyright © 2019 Chan Young Koh