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Articles 1 - 3 of 3
Full-Text Articles in Management Information Systems
Case Study: The Impact Of Emerging Technologies On Cybersecurity Education And Workforces, Austin Cusak
Case Study: The Impact Of Emerging Technologies On Cybersecurity Education And Workforces, Austin Cusak
Journal of Cybersecurity Education, Research and Practice
A qualitative case study focused on understanding what steps are needed to prepare the cybersecurity workforces of 2026-2028 to work with and against emerging technologies such as Artificial Intelligence and Machine Learning. Conducted through a workshop held in two parts at a cybersecurity education conference, findings came both from a semi-structured interview with a panel of experts as well as small workgroups of professionals answering seven scenario-based questions. Data was thematically analyzed, with major findings emerging about the need to refocus cybersecurity STEM at the middle school level with problem-based learning, the disconnects between workforce operations and cybersecurity operators, the …
Cyber Threat Intelligence Discovery Using Machine Learning From The Dark Web
Cyber Threat Intelligence Discovery Using Machine Learning From The Dark Web
Communications of the IIMA
Cyber threat intelligence (CTI) is an actionable information or insight an organization uses to understand potential vulnerabilities it does have and threats it is facing. One important CTI for proactive cyber defense is exploit type with possible values system, web, network, website or Mobile. This study compares the performance of machine learning algorithms in predicating exploit types using form posts in the dark web, which is a semi- structured dataset collected from dark web. The study uses the CRISP data science approach. The results of the study show that machine learning algorithms which are function-based including support vector machine and …
Crude Oil Prices Forecasting: Time Series Vs. Svr Models, Xin James He
Crude Oil Prices Forecasting: Time Series Vs. Svr Models, Xin James He
Journal of International Technology and Information Management
This research explores the weekly crude oil price data from U.S. Energy Information Administration over the time period 2009 - 2017 to test the forecasting accuracy by comparing time series models such as simple exponential smoothing (SES), moving average (MA), and autoregressive integrated moving average (ARIMA) against machine learning support vector regression (SVR) models. The main purpose of this research is to determine which model provides the best forecasting results for crude oil prices in light of the importance of crude oil price forecasting and its implications to the economy. While SVR is often considered the best forecasting model in …