Botnets represent one of the most powerful and dangerous forms of cyberthreats in today's digital world. The term "botnet" derives from the words "robot" and "network," denoting a network of infected devices controlled remotely by an attacker or group of hackers. These devices, often referred to as "zombies" or "bots," can include personal computers, smartphones, servers, IoT devices (such as smart cameras or routers), and even industrial systems. Infection occurs through malware, such as Trojans, viruses, or vulnerability exploits distributed via phishing, drive-by downloads, or infected applications.
Botnets are used for a variety of illegal purposes, from distributed denial-of-service (DDoS) attacks (when a network is overloaded with traffic to disable a website or service) to spam distribution, cryptocurrency mining, and, in particular, financial fraud such as carding. Carding is the process of using stolen credit or debit card information to make purchases, transfers, or other transactions. Card data is often stolen from company databases (through leaks, as in the case of major retail hacks), generated by algorithms, or purchased on the dark web. The role of botnets in scaling carding attacks lies in their ability to distribute tasks across thousands or millions of devices, allowing them to process massive amounts of data in parallel, minimizing the risk of detection and increasing efficiency.
In an educational context, understanding botnets is important for cybersecurity studies: they illustrate how distributed systems can be exploited for malicious purposes and highlight the need for strong security measures. Botnets are reported to generate billions of dollars in damage annually, affecting national economies and users' personal finances. For example, botnets like Simda have been used to harvest personal data, including card numbers, for subsequent testing.
Scalability is achieved through automation: a single botmaster can control millions of devices without significant overhead. In carding, this allows for real-time card testing, distributing the load to avoid IP blocks or rate-limiting from banks and payment systems.
As a result, botnets "supercharge" carding, allowing cards to be tested on sites with low verification levels (e.g., donations, microtransactions) without risk to the attacker. This leads to an increase in attacks: according to data, card testing attacks have increased by 200% in recent years due to botnets.
This method is conceptually similar to distributed computing in science (e.g., SETI@home), but it's designed for malicious purposes. In reality, it's vulnerable: banks use machine learning to identify patterns (anomalous traffic, correlations between requests).
Botnets are used for a variety of illegal purposes, from distributed denial-of-service (DDoS) attacks (when a network is overloaded with traffic to disable a website or service) to spam distribution, cryptocurrency mining, and, in particular, financial fraud such as carding. Carding is the process of using stolen credit or debit card information to make purchases, transfers, or other transactions. Card data is often stolen from company databases (through leaks, as in the case of major retail hacks), generated by algorithms, or purchased on the dark web. The role of botnets in scaling carding attacks lies in their ability to distribute tasks across thousands or millions of devices, allowing them to process massive amounts of data in parallel, minimizing the risk of detection and increasing efficiency.
In an educational context, understanding botnets is important for cybersecurity studies: they illustrate how distributed systems can be exploited for malicious purposes and highlight the need for strong security measures. Botnets are reported to generate billions of dollars in damage annually, affecting national economies and users' personal finances. For example, botnets like Simda have been used to harvest personal data, including card numbers, for subsequent testing.
Construction and management of botnets
To understand how botnets scale attacks, let's look at their architecture. A botnet typically consists of three components:- Command and control server (C&C): The central node where the attacker (botmaster) sends commands. C&Cs can be decentralized (P2P botnets, where bots communicate with each other) or centralized (IRC or HTTP-based). Modern botnets use fast-flux domains or even blockchain to disguise the C&C to avoid being taken down.
- Bots (zombie devices): Infected machines that execute commands. Infection often occurs through zero-day exploits, phishing emails, or malicious applications. Once infected, the bot connects to the C&C and awaits instructions. Bots can remain dormant until activated.
- Propagation infrastructure: Includes malware such as botloaders (e.g. based on Zeus or Emotet Trojans) that allow new devices to be added to the network.
Scalability is achieved through automation: a single botmaster can control millions of devices without significant overhead. In carding, this allows for real-time card testing, distributing the load to avoid IP blocks or rate-limiting from banks and payment systems.
The Role of Botnets in Scaling Carding Attacks
Carding attacks require two key steps: obtaining card data and validating it. Botnets are particularly useful in the second step, known as "card testing" or "card validation." Without distribution, the attack is limited by the resources of a single device: speed, IP address, and risk of detection. Botnets address this by providing:- Parallelism: Thousands of bots can test maps simultaneously, processing databases of millions of records in hours instead of days.
- Geographic diversity: Bots are located all over the world, mimicking traffic from different countries, making fraud detection (systems that identify fraud based on patterns, such as multiple requests from a single IP) difficult.
- Automation and adaptability: Bots can use scripts to mimic human behavior (e.g., adding random delays, changing the user agent), and integrate with proxies or VPNs for additional camouflage. Modern attacks utilize AI bots that learn to bypass defenses.
- Cost-effectiveness: Attackers "rent" botnets on the black market (botnet-as-a-service), paying for use, which lowers the barrier to entry for newcomers.
As a result, botnets "supercharge" carding, allowing cards to be tested on sites with low verification levels (e.g., donations, microtransactions) without risk to the attacker. This leads to an increase in attacks: according to data, card testing attacks have increased by 200% in recent years due to botnets.
Distributed Card Testing Method: A High-Level Overview
Distributed card testing is the process of validating data (card number, CVV, expiration date, and sometimes PIN or 3D Secure) by simulating transactions in a distributed environment. For educational purposes, we'll discuss it conceptually, focusing on the mechanisms, without going into technical implementation details. The method relies on distributed computing principles similar to cloud systems, but applied to their detriment.- Data preparation and task distribution: The attacker uploads a database of stolen or generated cards (e.g., using the Luhn algorithm to verify the validity of numbers) to the C&C server. The database is divided into chunks, which are distributed among bots. Each bot receives a task: a list of cards, testing targets (websites, payment gateway APIs), and parameters (e.g., transaction amount—usually $1-5 to avoid suspicion).
- Parallel execution: Bots execute requests independently. This includes:
- Simulating a purchase or donation on the target website (for example, through automated scripts that emulate a browser).
- Response processing: if the transaction goes through (the card is active), the bot marks it as valid; if it is rejected (for reasons such as insufficient funds or a block), it is discarded.
- Camouflage: Bots use IP rotation, random delays, different user agents, and even click/scroll emulation to appear like real users. AI bots can adapt, changing strategies based on user feedback.
- Results aggregation and analysis: Results (valid cards, errors) are sent back to the C&C. The server aggregates the data, filters out false positives, and possibly repeats the tests with other bots for confirmation. Valid cards are then used for real fraud or sold.
- Bypassing defenses: To avoid detection, the method includes techniques such as "slow drip" (slow, distributed requests), exploiting vulnerable sites, or integrating with other botnet functions (e.g., DDoS to distract attention).
This method is conceptually similar to distributed computing in science (e.g., SETI@home), but it's designed for malicious purposes. In reality, it's vulnerable: banks use machine learning to identify patterns (anomalous traffic, correlations between requests).
Examples and consequences
Well-known botnets used in carding include Emotet (for data theft) and Mirai (an IoT botnet for DDoS, but adapted for testing). Consequences include financial losses (billions of dollars), identity theft, and legal prosecution (the FBI often shuts down C&C systems). In Russia and internationally, botnets are regulated by cybercrime laws.Defense and educational conclusions
For protection:- Individually: Use antivirus software, two-factor authentication (2FA), transaction monitoring, credit freeze.
- Organizations: Implement bot detection (CAPTCHA, behavioral analysis), ML fraud systems, and cooperation with law enforcement.