A European retailer implements anti-fraud measures after a carding attack

[Student]

Hello! For educational purposes, I will detail the process of implementing anti-fraud measures at a European retailer after a carding attack. I will use a typical case study, similar to those found in public sources, such as those from companies providing cybersecurity solutions, and will supplement it with general knowledge about combating carding in eCommerce. I will describe the context of the attack, the measures the retailer implemented, the technologies used in the solution, and their impact on the business. I will also add educational aspects to explain how it works and why it is important.

Context: What is carding and how does it threaten retailers?​

Carding is a type of fraud in which criminals use stolen credit card information to make purchases or verify the validity of cards. In eCommerce, carding attacks are often automated: bots mass-test combinations of card numbers, expiration dates, and CVV codes on retailer websites to determine which cards are active. This can lead to several problems:

1. Chargebacks: Legitimate cardholders dispute unauthorized transactions, resulting in financial losses for the retailer as it is required to refund the money and often pays penalties to the payment processors.
2. Payment System Blocking: High levels of chargebacks can lead to a temporary suspension of payment processing by a processor (such as Visa or Mastercard), paralyzing online sales.
3. Reputational risks: Frequent attacks undermine customer trust in the platform's security.
4. Scraping: In addition to carding, scammers can use bots to collect data on prices, product availability, or user accounts, which increases the threat.

A typical example: in 2022, a major European fashion retailer operating in several countries (e.g., the UK, Germany, and France) experienced a series of carding attacks on its e-commerce platforms. The attacks targeted checkout pages, where bots tested thousands of stolen cards, leading to a temporary system overload, increased chargebacks, and loss of revenue from legitimate transactions.

Attack stages and retailer response​

1. Attack detection​

The attack began with unusually high traffic on checkout pages, which initially appeared to be a sales surge. However, analytics revealed:

• Anomalous traffic: Thousands of requests from different IP addresses, most of which used a proxy or VPN.
• High transaction failure rate: Payment gateways rejected numerous payment attempts due to invalid card details.
• Data scraping: Bots collected information on prices and product availability, indicating preparation for larger-scale attacks.

The retailer realized that inaction would lead to financial losses and the risk of being blocked by payment systems. This became the catalyst for implementing anti-fraud measures.

2. Selecting a solution​

After analyzing the market, the retailer decided to implement a solution from a company specializing in bot protection, such as Netacea (a real-world example often cited in eCommerce case studies). This solution was chosen for its ability to:

• Integrate without significant changes to the site infrastructure.
• Use artificial intelligence to analyze user behavior and identify bots.
• Provide data from the dark web and hacker forums for proactive protection.

Implemented anti-fraud measures: in detail​

The retailer has implemented a comprehensive approach, broken down into several key measures. I'll describe them in detail, explaining how they work, and include educational aspects for understanding.

1. Agentless integration​

What is it? A solution like Netacea integrates via a cloud platform or API, requiring no software installation on the retailer's end. This is achieved by analyzing traffic through a proxy or integrating with existing infrastructure (such as a CDN like Cloudflare).

How does it work?

• Traffic is routed through a security layer that analyzes requests in real time.
• The system collects data on user behavior (e.g., input speed, mouse movement, action sequence) and compares it with bot patterns.
• Legitimate users are allowed through without delay, while suspicious requests are blocked or redirected to additional verification (such as CAPTCHA).

Advantages:

• Minimal impact on site performance (important for eCommerce, where a 100ms delay can reduce conversion by 1%).
• Quick installation: implementation takes from a few hours to a couple of days.
• Compatibility with websites, APIs, and mobile apps.

Educational aspect: The agentless approach minimizes development and maintenance costs, which is especially important for retailers with limited IT resources. It also allows for scalable protection without the need to rewrite website code.

2. AI engine for intent detection​

What is it? Artificial intelligence that analyzes user behavior and determines their intent (legitimate purchases vs. fraudulent activity). Machine learning is used to recognize patterns characteristic of carding bots.

How does it work?

• Data Collection:AI analyzes hundreds of signals, including:
  • Geolocation and IP addresses (e.g. use of anonymizers or data centers).
  • Devices and browsers (bots often use outdated or minimalist browsers).
  • Behavioral metrics (bots act faster and more linearly than humans).
• Classification:Algorithms assign a "risk score" to each query. For example:
  • Legitimate user: risk score 0-20.
  • Suspicious bot: risk score 80-100.
• Response: Requests with a high risk score are blocked, redirected for review, or rejected.

Example: If a bot attempts to test 100 cards in a minute from a single IP address, the AI will recognize the anomaly (too high a request rate) and block the IP address or all traffic from that device.

Benefits:

• High accuracy: Solutions like Netacea claim to be 33 times more effective than competitors thanks to predictive analysis.
• Adaptability: AI learns from new types of attacks, including previously unknown ones (zero-day threats).
• Reduced false positives: legitimate users are not blocked, which maintains conversion rates.

Educational aspect: AI in anti-fraud systems uses a combination of supervised learning (training on labeled data from past attacks) and unsupervised learning (anomaly detection without prior labeling). This allows the system to adapt to evolving threats, such as new generations of bots that mimic human behavior.

3. Monitoring forums and the dark web​

What is it? The threat intelligence service collects data from hacker forums, Telegram channels, and the dark web, where fraudsters exchange stolen cards and discuss retailer vulnerabilities.

How does it work?

• Automated scanners monitor thousands of sources on the dark web.
• Mentions of the retailer, its competitors, or new attack methods (for example, updated card lists or bot scripts) are analyzed.
• The data is integrated into the anti-fraud system to update blacklists of IPs, devices, or behavior patterns.

Example: If a list of stolen cards appears on the dark web, the system can proactively block attempts to use them on the retailer's website.

Benefits:

• Proactive protection: the retailer learns about threats before they occur.
• Context for analysis: Understanding which types of attacks are popular helps adjust your defense strategy.

Educational aspect: The dark web is a part of the internet accessible through specialized browsers (such as Tor), where stolen data, including credit cards, accounts, and exploits, is sold. Monitoring these sources requires specialized OSINT (Open-Source Intelligence) tools and skills, making such solutions valuable for large retailers.

4. Real-time visualization and analysis​

What is it? Interactive dashboards that display attack metrics such as the number of blocked requests, attack geography, device types, etc.

How does it work?

• Data is collected in real time and displayed in a user-friendly interface.
• The retailer can see, for example:
  • How many bots were blocked in the last hour.
  • Which website pages are attacked most often (for example, checkout or login).
  • Distribution of attacks by country or IP address.
• Analytics helps make decisions: for example, strengthening protection at certain stages of the sales funnel.

Advantages:

• Fast response: The security team can adjust settings instantly.
• Transparency: The retailer understands which threats are most pressing.
• Optimization: Reducing false positives by fine-tuning rules.

Educational aspect: Data visualization is a key element in modern security systems. It simplifies analysts' work, allowing them to quickly identify trends and anomalies. Technologies used, such as the ELK Stack or proprietary platforms, aggregate logs and transform them into actionable insights.

Implementation results​

After implementing anti-fraud measures, the retailer achieved the following results:

1. Complete protection against carding attacks: Repeated card testing attempts were blocked, and the number of chargebacks was reduced to a minimum.
2. Reduced scraping: Bots attempting to collect price and product data have been neutralized.
3. Conversion Preservation: Legitimate users experienced no inconvenience because the protection was "invisible" (no unnecessary CAPTCHA or delays).
4. Improved reputation: The retailer became a less attractive target for fraudsters, which reduced the burden on payment systems and strengthened customer trust.
5. Resource savings: Automated protection has reduced the need for manual attack analysis.

Educational Takeaways: Why Are They Important?​

1. Evolving threats: Carding attacks are becoming increasingly automated and sophisticated. Bots use machine learning to mimic human behavior, requiring retailers to utilize advanced technologies such as AI and threat intelligence.
2. Balancing security and UX: Anti-fraud measures should not degrade the user experience. For example, excessive use of CAPTCHA can discourage customers. Solutions such as agentless integration allow you to protect your website without sacrificing conversion rates.
3. Economic Impact: According to research (such as that from Juniper Research), eCommerce fraud costs retailers billions of dollars annually. Chargebacks, fines, and customer loss are direct losses that can be minimized with the right tools.
4. Proactive approach: Monitoring the dark web and forums allows retailers to stay ahead of scammers. This is an example of the shift from reactive defense (reacting to an attack) to proactive (preventing an attack).
5. Tech stack:Modern anti-fraud systems combine several technologies:
   • Machine learning: For behavior analysis and anomaly detection.
   • Big Data: For processing huge volumes of traffic data.
   • Cloud Computing: For scalability and rapid deployment.
   • OSINT: To collect data from open and closed sources.

Recommendations for retailers​

For those who want to implement similar measures, here are the steps you can take:

1. Vulnerability Audit: Conduct an analysis of the current infrastructure to identify weaknesses (e.g., unsecured APIs or checkout pages).
2. Choosing a solution: Consider platforms such as Netacea, DataDome, Akamai Bot Manager, or PerimeterX, which specialize in eCommerce security.
3. Payment System Integration: Ensure your anti-fraud solution is compatible with your PSP (Payment Service Provider) to minimize chargebacks.
4. Team Training: Invest in training cybersecurity professionals so they can effectively use dashboards and analytics.
5. Regular updates: Fraudsters are constantly changing their tactics, so anti-fraud systems must be updated in real time.

If you have any specific questions about the technology, the implementation process, or other aspects, let me know and I'll go into more detail!