Portfolio / Projects / digital-forensics-investigation

Cybersecurity · Digital forensics · Team academic project

Reconstruct the NARCOS-2019 scenario from digital evidence.

As part of the NARCOS-2019 scenario, I contributed to a full digital-forensics investigation across three seized systems: acquisition, disk and memory examination, artefact correlation and final reporting.

3Systemsdisk + memory analysis
4NIST phasescollection, examination, analysis, reporting
1Timelinerebuilt from artefacts
1Final reportstructured and evidence-based

Tools & technologies

AutopsyFTK ImagerVolatilityWiresharkHxD

Tools used across the NARCOS-2019 scenario and supplementary labs.

01 / Context

A simulated trafficking case handled as a structured investigation.

The scenario focused on a drug-trafficking operation between Australia and New Zealand. Three digital systems were provided for analysis: John Fredricksen’s laptop, Jane Esteban’s laptop and Steve Kowhai’s desktop.

The goal was not only to extract files. The work was to connect artefacts, understand each actor’s role and rebuild a coherent, defensible narrative.

02 / Scope

Three systems, multiple types of digital artefacts and a process of cross-checking evidence.

Systems3

two laptops and one desktop

SourcesDisk + memory

files, caches, processes, network traces

Core questionWho did what?

coordination, surveillance, concealment

The scope covered system artefacts, Discord caches, steganography-related elements, deleted files, malware indicators and all the traces needed to rebuild a reliable sequence of events.

03 / Method

A simple workflow: collect cleanly, examine, correlate, then report.

01

Collection

Work based on disk images and memory captures provided as part of the scenario.

02

Examination

Explore file systems, recover artefacts and inspect volatile memory.

03

Analysis

Correlate clues across devices to confirm roles, links and the sequence of actions.

04

Reporting

Produce a structured report presenting findings, limits and conclusions.

Tools

Disk analysis

Autopsy and FTK Imager for files, metadata, browser traces and cached communications.

Tools

Memory analysis

Volatility for processes, network sessions and malware-related evidence in RAM.

Control

Cross-checking

Compare evidence across devices, communication logs and time-based artefacts before drawing a conclusion.

Autopsy view of the Downloads folder in the NARCOS-2019 disk image: Quasar folder and downloaded files.
Autopsy: Downloads folder inspection showing Quasar-related artifacts among recovered files. Interpretation requires corroborating evidence.

04 / Findings: from evidence to conclusion

The investigation becomes understandable when artefacts from several systems converge on the same story.

CommunicationsDiscord caches connect the actors and the preparation

Recovered exchanges provide the context needed to link files, timestamps and actions to specific people.

ConcealmentData hidden inside images complements the communications

Steganography matters because it aligns with conversations, recovered files and the wider timeline rather than standing alone.

SurveillanceQuasar RAT artefacts support the undercover-surveillance role

Autopsy and memory artefacts on Jane Esteban’s machine are consistent with surveillance activity targeting John Fredricksen’s system.

CorrelationThe three systems tell one coherent story

Browsing, communications, files, memory and metadata converge enough to rebuild roles and sequence.

Autopsy artefacts related to Quasar RAT in the NARCOS investigation
Example evidence: Quasar RAT-related artefacts become meaningful once connected to the role and timeline supported by other traces.

05 / Timeline

A timeline rebuilt from digital traces.

Recovered messages, browsing history, downloaded files, steganography-related artefacts and memory activity were aligned to reconstruct the sequence of actions: preparation, communication, concealment attempts and logistical coordination.

  • Preparation and exchanges between actors through Discord.
  • Research and use of steganography tools to hide information inside images.
  • Quasar RAT-related elements in the context of surveillance against John Fredricksen.
  • Consistency between traces recovered on the three devices and the scenario narrative.

06 / Complementary labs

The focused labs follow the same logic: start from a technical trace and explain what it establishes.

Windows forensicsRebuild activity from the MFT and browser artefacts

MFTECmd, Timeline Explorer and Autopsy recover a downloaded ZIP, its origin through Zone.Identifier, then related browsing and autofill traces.

Windows MFT and artefact analysis
MFT and download metadata help rebuild a timeline rather than inspect one isolated file.
Linux memory forensicsConnect the malicious binary to execution and persistence

Memory analysis identifies youtube_downloader.elf, its processes, the persistence path /usr/bin/nettstat, remote IP 64.23.144.215 and attacker-created account systemservice.

Linux memory analysis with Volatility
The useful result is the chain execution → child process → persistence → remote source.
Network forensicsAttribute activity by combining independent network signals

In the Nitroba PCAP, source IP, DNS queries, HTTP cookies and MAC address converge on Johnny Coach as the sender of the harassing emails.

Wireshark analysis in the Nitroba lab
Attribution becomes credible because several independent traces point to the same user.

07 / Conclusion

The common thread across these DFIR exercises is correlation: one artefact is rarely enough.

NARCOS-2019 and the complementary labs taught me how to move from scattered technical artefacts to a timeline and evidence-based explanation. The value lies as much in evidence integrity and cross-checking as in the tools used to extract it.

Profile value

This practice reinforces how I approach data in general: verify the source, preserve traceability, separate observation from interpretation and build only the conclusions the evidence can support.

Next project

SMS Spam Detection: compare a sequence model with a simple NLP baseline.