Malware AnalysisMalware AnalysisMalware Analysis

Examples of everyday tasks

• Review sandbox reports and summarize a sample’s behavior.
• Extract indicators and share them with detection teams.
• Compare samples to find related malware families.
• Reverse engineer key parts of a program to confirm what they do.
• Write reports that explain technical findings clearly.

Related job titles

• Malware analyst
• Reverse engineer

These are career areas to explore, not a checklist. Some of these job titles usually require experience, and completing a starter project does not by itself make someone ready for them.

Starter project

Compare Two Malware Analysis Reports

Use published analysis reports or supplied sandbox reports. Compare behavior, indicators, and differences.

This beginner project works only from published or supplied reports. It does not require downloading or running live malware.

Comparing reports is report analysis, not hands-on reverse engineering. Describe it that way.

What you’ll use

• Two published malware analysis reports from government agencies or security researchers, or supplied sandbox reports
• A comparison table in a spreadsheet or document

Resources to start with

• CISA Cybersecurity Alerts & Advisories (opens in a new tab)

  Public advisories from the Cybersecurity and Infrastructure Security Agency, including malware analysis reports.

• MITRE ATT&CK (opens in a new tab)

  A shared vocabulary for describing the behaviors you compare.

More projects for this path

Each one comes with the framework to follow, five name ideas and a walkthrough for publishing it on GitHub.

• Malware Report Comparison

  Compare two published analyses of related samples and publish a cited table.

• Sandbox Report Summary

  Turn a long public sandbox report into a one-page summary defenders can use.

Project walkthrough

1. Choose two reports about related samples, such as two versions of the same malware family or two families with a similar goal.
2. Record each report’s publisher, date, and link.
3. Summarize each sample’s behavior: how it arrives, what it changes, and what it communicates with.
4. Build a table comparing behaviors and indicators side by side.
5. Note the differences and whether the reports explain them.
6. Separate the researchers’ findings from your own interpretation, and cite each claim.

What to publish

• A cited comparison of the two reports
• A table of behaviors and indicators, with web addresses written so they cannot be clicked (for example, example[.]com)
• A clear separation between the original researchers’ findings and your interpretation

Optional extension

Explain what additional evidence would be needed to confirm an uncertain conclusion.

Example résumé bullet

Numbers are what make a bullet worth reading, so count them while you work: how much data you searched, how many findings you fixed, how many tests you added. Every figure should be one a reader can find in your report. Write the line after the work is done, and leave out impact or experience you cannot show.

Numbers to record while you work

Project name | tools, datasets and frameworks | link

Give the project a name of its own: “Petri” is an example, not a label to copy. List the tools, datasets and frameworks you actually used, since several relevant ones show more than a single tool does. Hyperlink the last part to your published repository or report.

Turn your project into a portfolio piece

A project becomes easier to evaluate when someone can understand what you did, inspect the evidence, and follow your reasoning.

Create a short report, GitHub README, or portfolio page. A clear two-to-four-page report is a useful starting point, not a strict requirement.

Include:

1. Goal: What question or problem did you investigate?
2. Environment: What tools, practice data, or systems did you use?
3. Your work: What did you personally do, and why?
4. Evidence: Include useful screenshots, queries, code, or test results.
5. Findings: What did you discover? What remains uncertain?
6. Fix or recommendation: What should change, and did you test it?
7. Sources and limitations: Credit datasets and guides. Clearly identify simulated work.

Use sample or redacted information in the public version.

Publish the report somewhere accessible without requesting permission, and check the link before putting it on a résumé.

Put numbers in it

Count as you work, and put the figures in the report: records searched, findings confirmed, tests added, checks failing before and after. A reviewer can check every one of them against your evidence, which is exactly why they are worth including. Leave out business impact you cannot show.

Name it, then show your tools

A résumé line works best in three parts: your project’s own name, the tools and frameworks you used, and a link to the work. “Role-Based Access Lab” reads like a class exercise. A name plus a tool list tells a reviewer what you can actually work with, and every tool, dataset or framework you list honestly is another thing an interviewer can ask you about.

Project name | tools, datasets and frameworks | link

• Role-Based Access Lab→Turnstile | Keycloak, RBAC design, NIST SP 800-63, Docker | GitHub
• Login Hunt→Beacon | Python, pandas, Sigma, MITRE ATT&CK | GitHub
• Cloud Project→Bedrock | Terraform, Checkov, AWS IAM, CIS Benchmarks | GitHub

Why link a report?

Your résumé gives a short description of the project. The linked report lets a reviewer inspect your work and gives you concrete evidence and decisions to discuss during an interview.

Not every recruiter will open it, but it makes your work available for review.

Hyperlink the last part to the published work, so “GitHub” or “Project Report” opens your repository or report. Check the link before you send the résumé.

# Project title

> Practice project. Describe the simulated scenario, practice data, or lab used.

## Goal
What question or problem did you investigate?

## Environment
What tools, practice data, or systems did you use? Link datasets and guides.

## My work
What did you personally do, and why?

## Evidence
Screenshots, queries, code, or test results. Use sample or redacted information only.

## Findings
What did you discover?
The numbers behind it (records searched, findings confirmed, tests added).
What remains uncertain?

## Fix or recommendation
What should change, and did you test it?

## Sources and limitations
- Datasets and guides used:
- What was simulated:
- Limitations:

Related paths to explore

• Digital Forensics

  Digital forensic analysts examine computers, phones, and other devices to reconstruct what happened, handling evidence carefully so their conclusions can be checked.

• Threat Intelligence

  Threat intelligence analysts research who is attacking organizations, how, and why, then turn that research into practical guidance for defenders.

• Threat Hunting / Detection

  Threat hunters search activity records for attackers that existing alerts missed, and detection engineers turn what they learn into rules that catch the behavior automatically.