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The Impact of Cyberattacks and Cyberthreats on Higher Education Institutions.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	The Impact of Cyberattacks and Cyberthreats on Higher Education Institutions./
作者:	Jackson, Michael.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2021,
面頁冊數:	60 p.
附註:	Source: Masters Abstracts International, Volume: 83-02.
Contained By:	Masters Abstracts International83-02.
標題:	Information technology. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28644975
ISBN:	9798522970451

The Impact of Cyberattacks and Cyberthreats on Higher Education Institutions.
Jackson, Michael.

The Impact of Cyberattacks and Cyberthreats on Higher Education Institutions. - Ann Arbor : ProQuest Dissertations & Theses, 2021 - 60 p.

Source: Masters Abstracts International, Volume: 83-02.

Thesis (M.S.)--The College of St. Scholastica, 2021.

This item must not be sold to any third party vendors.

Colleges' and universities' prominent, high-risk nature places them among the most at-risk targets for cyberattacks worldwide. While effective solutions exist to detect suspicious activities and block them promptly, cyberattacks continue to increase. This study addresses cyberattacks' impact-notably those directed at higher education institutions-by analyzing historical cyberattack trends and predicting potential future cyberattack occurrences on a college's network. The study analyzes two datasets: The College of St. Scholastica Threat Logs (CSS Threats) and the Canadian Institute for Cybersecurity's Intrusion Detection Evaluation Dataset (CIC-IDS2017). CSS Threat Logs is proprietary and novel dataset from The College of St. Scholastica used only in this study; CIC-IDS2017 is a cybersecurity research dataset used in other studies. Domo, Google Colab, Python, NumPy, pandas, and scikit-learn were used to analyze the datasets and produce results answering the study's research questions. Regression models were fit to predict near-future cyberattack occurrences in CSS Threat Logs. Classification models were fit to model and score benign traffic and individual threat groupings in CIC-IDS2017. The study's results found that several sudden increases in cyberthreat occurrences throughout CSS Threat Logs were often associated with a significant number of threats within a single category. The study also found that most cyberthreats originated from the United States and were categorized as info-leak, brute-force, or code-obfuscation. The CSS Threat Logs predictive model can be used to anticipate near-future cyberthreats. The application of CIC-IDS2017 models can improve prediction models and cybersecurity monitoring metrics related to threats within common cyberthreat groups.

ISBN: 9798522970451Subjects--Topical Terms:

532993
Information technology.
Subjects--Index Terms:

Cyber security

The Impact of Cyberattacks and Cyberthreats on Higher Education Institutions.
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