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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.
Course Instructor: Ashish Revar
Unit 1
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 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.
Unit 3
Where research data actually comes from and how trustworthy the instrument collecting it is, how to sample a population honestly, how to state and test a hypothesis without overclaiming, what scale of measurement a given number actually is, which statistical test a question calls for, and the tools used to run it.
Unit 4
How a research report is structured and why each section exists, how to write an introduction that states a genuine problem rather than summarising background, how to turn a list of papers into a literature review that exposes a gap, how to write a methodology section that satisfies the "valid and verifiable" test, how to present results honestly and interpret them without overclaiming, how to handle citations and referencing across the formats a researcher is likely to encounter, and how to write an abstract that stands alone and a conclusion that synthesises rather than restates.
Unit 5
The ethical foundations every security and ML researcher is accountable to — from the Belmont Report's three principles and India's DPDP Act 2023 to FFP research misconduct, the Section 3(k) limit on patenting algorithms, and Google Project Zero's 90+30 responsible disclosure window — culminating in a full ethics audit of the five-chapter research journey.
