Evidence from Nowhere: Digital Forensics in Carding Cases (From Telegram Chat Seizures to Blockchain Analysis)

[Professor]

Introduction: Hunting for Digital Phantoms
Investigating carding is like assembling a puzzle, the pieces of which are scattered across dozens of servers in different countries, encrypted, and owned by anonymous individuals. Traditional evidence — fingerprints, murder weapons — is replaced by digital artifacts. The goal of digital forensics is to find, extract, and connect these artifacts into an irrefutable chain of evidence leading from an anonymous username to a real person. This work is at the intersection of technology, law, and detective logic.

Chapter 1: Getting Started: Device Forensics​

It all starts with a physical medium: a phone, a laptop, a server on a rented hosting service.

Key artifacts and methods:

1. Memory dump (RAM acquisition): Allows you to obtain active data : unencrypted passwords, decryption keys, open messenger sessions (even in Telegram "secret chats" if the device was active). This is a gold mine for investigators.
2. File system analysis: Searches not only for obvious files, but also for deletion artifacts.
   • Remnants of messenger databases (local copies of chats, contacts, even if the history is “cleared”).
   • Thumbnail image caches are thumbnails of images that may have been viewed or sent, even if the originals have been deleted.
   • Logs of installation and removal of programs (which applications related to carding were used).
3. Cryptography and password cracking:
   • Dictionary and brute-force attacks to crack passwords for archives, containers (Veracrypt), or files.
   • Analysis of cached credentials in browsers.
   • Using hardware keys (for example, via JTAG ports on phones) to dump memory while bypassing the lock.

Discovery: Even paranoid carders who adhere to OPSEC leave traces of behavior patterns on the device: activity schedule, geolocation (in EXIF data of photos or system logs), and familiar Wi-Fi networks.

Chapter 2: Networks and Clouds: Traffic Analysis and ISP Data Extraction​

When the device is "clean", forensics goes online.

1. Network Traffic Analysis (Network Forensics):
   • Internet service provider logs (ISP logs): The IP address and time are used to determine the connection to specific resources (forums, phishing site hosting).
   • Packet capture: If you have access to network equipment, you can intercept unencrypted traffic (rare) or establish connections to botnet command-and-control (C&C) servers.
2. Working with service providers:
   • Telegram, Discord: Upon official request from law enforcement agencies (often through international cooperation through the Mutual Legal Assistance Treaty (MLAT)), they may provide IP login logs and metadata (phone numbers linked to accounts, number change history). However, chat content is not available if it is encrypted.
   • Hosting providers: Seizure of servers hosting phishing sites, checkers, and databases. Analysis of access logs to these servers.
   • Email: Gain access to email accounts used for registrations.

Complexity: Time zones and jurisdictions. Data may be destroyed by the provider upon expiration of the retention period while the bureaucratic request process is underway.

Chapter 3: Crypto-Forensics: Tracking the Untrackable​

The Bitcoin or Ethereum blockchain is not anonymous, but pseudonymous. It's an open ledger, where every transaction is recorded forever. The cryptoanalyst's job is to deanonymize the entries in this ledger.

Blockchain analysis methods:

1. Address clustering: Combining multiple addresses into a single walletbelonging to a single entity. Based on pattern analysis:
   • Common Spend Heuristic: If multiple inputs are used in a single transaction, they are likely owned by the same owner (since both private keys are needed).
   • Change address heuristic: Analyzing the outputs of a transaction to determine which of them is change sent back to the sender.
2. Identifying on-ramp/off-ramp points: Key! You need to find where crypto entered (was purchased with fiat) or exited(was exchanged for fiat) the system.
   • Analysis of deposits/withdrawals on regulated exchanges (Binance, Bybit, etc.). The exchange will provide KYC data for the account owner upon request.
   • Tracking P2P transactions: If the carder used a P2P platform, you can identify counterparties and request their data.
3. Mixer Analysis:
   • Modern tools (such as those from Chainalysis or CipherTrace) can statistically trace the path of funds even through mixers like Tornado Cash with a high probability, especially if an error was made at the input or output (such as sending an overly large amount or reusing an address).
   • Time and total marks: Comparison of time and total at the input to and output of the mixer.

The proof chain in crypto: *Phishing victim -> transfer to exchange A -> withdrawal in BTC -> transfer through 5 wallets -> deposit to exchange B (with Ivan Ivanov's KYC) -> withdrawal to Ivan Ivanov's card -> payment of his apartment rent.* The blockchain connects the first and last elements of this chain.

Chapter 4: Building a Digital Dossier: Linking Artifacts​

Individual clues are meaningless. The value lies in their intersection.

An example of a digital portrait reconstruction:

1. A Telegram number and fragments of correspondence with the nickname "Lestat" were found on the detained dropper's phone.
2. Telegram's provider uses this number to find a history of IP addresses used for logins. One of the IP addresses belongs to a rented server in the Netherlands.
3. A phishing campaign control panel and log files were found on a server in the Netherlands (seized through local police). The logs contain references to the same "Lestat" username and a crypto wallet used for collecting donations.
4. Blockchain analysis shows that funds from this wallet were sent to exchange "X." A request to exchange "X" yields the passport details of Russian citizen P.P. Petrov.
5. An analysis of Petrov P.P.'s devices (during the search) shows the use of the same specific encryption software, traces of which were found on the server in the Netherlands, and a saved password for the "Lestat" account in a password manager.

The chain is closed: Dropper -> operator (nickname) -> infrastructure (server) -> financial flow (crypto) -> real identity (KYC exchanges) -> digital habits (software, passwords).

Chapter 5: Legal Challenges and the Future​

Problems:

1. Speed vs. bureaucracy: While a request is being sent to another country, digital traces can disappear.
2. Encryption: End-to-end encryption of messaging apps makes the content of correspondence inaccessible. Evidence is sought "around" it — in metadata and on endpoint devices.
3. DeFi and Privacy Coins: Investigating transactions in fully decentralized finance (DeFi) and using Monero (XMR) is an extremely complex, and often impossible, task for current forensics.
4. Data Volume: The need to process terabytes of information requires automation and the use of AI to identify patterns.

The Future: The development of proactive digital forensics — the creation of "digital twins" of criminal networks based on collected data to predict their actions. AI is already helping to find connections between wallets and accounts that are not obvious to humans. The battle is shifting to artificial intelligence and big data analytics.

Conclusion: Light in the Digital Labyrinth
Digital forensics in carding cases is not magic, but meticulous craftsmanship with digital fingerprints. It proves that in the age of total anonymity, the perfect crime does not exist. Every action leaves noise in metadata, a trace in logs, or a record in the blockchain.

The main conclusion: the modern carder is not fighting an abstract "law," but the physics of data transmission, the mathematics of cryptography, and the inexorable logic of traces left behind. And as long as he's a human being, making mistakes, leaving digital shadows, and seeking to cash out his virtual loot in the real world, digital forensics will have the opportunity to drag him out of nowhere — into the realm of legal responsibility.