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Research Methodology
Research Methodology
A practical guide to research methodology for data science, machine learning, and cybersecurity students: what counts as genuine research, how to define and narrow a research problem, and how to defend a study's scope and limitations, grounded throughout in real cases like WannaCry and Mirai.
Instructor: Ashish Revar
Course syllabus
Unit 1
Introduction to Research
What separates genuine research from information-gathering, the different types of research, how research happens in malware analysis and security machine learning, the seven-stage research process, how to define and narrow a research problem, and why every study needs a stated scope and limitations.
Unit 2
Research Design and Literature Review
Research design types from purely descriptive to true experimental, the literature review process from search string to named gap, systematic reviews versus narrative ones, the academic databases and reference tools researchers actually use, and why reproducibility failures are already well documented inside malware research itself.
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Identifying and Defining a Research Problem
"IoT security" is a topic. It is not a research problem. The Mirai botnet shows what reframing a question actually looks like, and how splitting a big question into two or three sub-problems turns a news story into something you can actually answer.
Read article research methodologyResearch in Malware Analysis and Security Machine Learning
Three approaches dominate this field: empirical, theoretical, and applied engineering. Marcus Hutchins' discovery of the WannaCry kill switch shows what applied research looks like when it happens in real time, with the world watching.
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