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Project 02

Failed-Login Hunt

Search a public authentication dataset for password spraying and prove or disprove it.
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Difficulty
Medium
Time
5 to 8 hours
On a résumé
Solid portfolio piece

Why this one is worth your hours

Shows you can start from a hypothesis, query real data and stay honest about limits.

Two or three sessions. Enough depth to carry an interview answer.

What you walk away with

  • LANL authentication dataset
  • Python
  • pandas
  • Sigma

Those names go on your résumé line, and every one of them is something an interviewer can ask you about, so use them honestly.

Give it a name of its own

“Failed-Login Hunt” describes the work. It is not the name. Pick one of these, or invent your own, then use it everywhere: the repository, the README title and the résumé.

  • Beacon
  • Lowlight
  • Spray Watch
  • Hunt Log
  • Nightshift

The framework you follow

  • MITRE ATT&CK T1110

    Frame the hunt around brute force and password spraying behaviour.

  • Sigma

    Write the finished detection in a format other tools can import.

How to follow a framework, honestly

A framework is a checklist someone argued about for years. You do not read all of it. You use the part that matches your project.

  1. Take one section. Open the framework and find the section that covers your project. Read only that.
  2. Quote the requirement. Put the requirement in your README in its own words, one or two lines, with a link.
  3. Map your steps to it. Beside each step you take, name the item it satisfies. This is what makes the work checkable.
  4. Record where you differ. You will skip something because it needs a budget or a team. Write that down and why. It reads as judgment, not as a gap.

Naming a framework you followed and showing where you diverged is worth more in an interview than listing five you have only heard of.

Do the work

Keep a note file open. One line per step: what you ran, what came back, what you decided.

  1. Write the hypothesis: repeated failures from one source, then a success.
  2. Read the dataset documentation and note what each field means.
  3. Query for sources with many failures across many accounts.
  4. Check whether a success followed, and how quickly.
  5. Rule out ordinary causes: expired service passwords, shared addresses, typos.
  6. Draft a Sigma rule and count how often it fires on a normal day.

What ends up in the repository

  • The hypothesis and every query you ran
  • Findings with the evidence tables
  • The Sigma rule and its false-positive count

Put it on GitHub

This is the part people skip, and it is the part a reviewer actually opens. Eight steps, about twenty minutes the first time.

  1. Create the repository

    github.com → New repository

    Name it the project name you picked. Add a README and an MIT licence. Keep it public.

    Note it down Write the chosen name in your notes. It goes on your résumé later, unchanged.

  2. Bring it to your machine

    git clone https://github.com/you/arkham.git
cd arkham

    The terminal prints Cloning into 'arkham'. You now have a folder with one README in it.

    Note it down If it asks for a password, set up a personal access token or SSH key once and never again.

  3. Make room for evidence

    mkdir evidence notes scripts
printf 'data/\n*.pcap\n' > .gitignore

    Three empty folders and a .gitignore, so raw captures and downloaded datasets never get committed.

    Note it down Never commit real data, keys or anything with someone's name in it.

  4. Write the README before the work

    open README.md

    Goal, environment, what I did, evidence, findings, limitations. Headings first, blanks underneath.

    Note it down Writing the headings first tells you what to collect while you work, instead of after.

  5. Take notes as you go

    notes/log.md

    One line per action: the time, what you ran, what came back, what you decided.

    Note it down This is where your numbers come from. In three days you will not remember why you ruled something out.

  6. Commit in small pieces

    git add .
git commit -m "Add triage table for alerts 1 to 6"

    Each commit is a checkpoint with a message you can read back months later.

    Note it down One commit per step of the project. An interviewer can scroll your commits and see how you worked.

  7. Push it up

    git push

    Refresh the page on github.com and your README is the front page of the project.

    Note it down Check it in a private window. If it 404s, the repository is still private.

  8. Finish it properly

    git tag v1.0
git push --tags

    Screenshots in evidence/, the real numbers filled into the README, a tagged version.

    Note it down Copy the repository link into your résumé line and click it once before you send it.

  • Small commits beat one giant upload; the history is part of the evidence.
  • Every screenshot needs a caption saying what it proves.
  • If something did not work, keep it and say so. Reviewers trust write-ups that admit limits.

The résumé line

Project name | tools, datasets and frameworks | link

Example, with your own name and your own numbers

BeaconLANL authentication dataset, Python, pandas, Sigma, MITRE ATT&CK T1110GitHub

Hunted 1.4 million authentication records for password spraying; 6 queries isolated 1 source trying 3 passwords across 400 accounts, and the resulting rule fired 0 times on a normal-day baseline.

The numbers come from your notes, not from this page. Count as you work, keep every figure checkable in your report, and hyperlink the last part to the repository.

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