Hackers are plotting to benefit from the generosity of Halloween, Thanksgiving, and Christmas shoppers using carding attacks as we gear up for the holiday season shopping.
Online companies selling products or services are struggling with the growing issue of carding. Juniper Research predicts retailers could lose $130 billion to card-not-present (CNP) fraud by 2023.
To ensure a secure online holiday experience, let’s uncover and understand what a carding attack is and how to protect against it.
What is a Carding Attack?
Carding attacks primarily target information embedded in payment cards, such as credit or debit cards. The attackers, known as carders, employ various techniques to obtain this data, which includes the cardholder’s name, card number, expiration date, and the security code (CVV/CVC). Check out how carding fraud works in detail.
How Does Carding Attack Affect E-commerce?
With more people shopping online, cybercriminals take advantage of the situation by using stolen card details without even needing the physical card.
To make matters worse, they’ve figured out how to get around a security feature called the Card Verification Value (CVV), a secret code on your card. This code ensures that the person making a purchase has the real card, but these cybercriminals have found ways to outsmart it.
Carding attacks in e-commerce manifest in several common scenarios:
Stolen Credit Card Verification:
Fraudsters utilize automated bots to verify the validity of stolen credit card details through inconspicuous test purchases on various e-commerce platforms. This discreet validation allows them to confirm the cards’ authenticity before committing more substantial fraudulent activities.
Fraudulent Transactions:
Cybercriminals exploit stolen credit card information to execute large-scale, unauthorized transactions on e-commerce websites. This use case results in financial losses for targeted online retailers and poses a significant threat to the overall security of digital transactions.
Gift Card Balance Theft:
Carders target gift card systems, attempting to use stolen credit cards to purchase gift cards and subsequently drain their balances. This tactic allows cybercriminals to convert stolen credit card information into easily transferrable and monetizable gift card assets.
Account Takeover for Purchases:
Fraudsters gain unauthorized access to user accounts on e-commerce platforms, utilizing saved payment information to make fraudulent purchases. This carding attack involves compromising user credentials to exploit the account owner’s financial resources.
Refund Fraud:
Carders exploit the refund process by purchasing stolen credit cards and requesting refunds. This tactic allows cybercriminals to use the e-commerce platform’s refund mechanisms to extract funds or merchandise.
Bulk Purchases for Resale:
Cybercriminals engage in large-scale purchases of high-value items using stolen credit cards, intending to resell the goods for profit. This use case combines the financial impact on the targeted e-commerce platform with the potential for secondary gains through the resale of fraudulently acquired items.
How to Prevent Carding Attack?
Detecting carding attacks requires a combination of advanced technologies, behavioral analysis, and proactive monitoring. Here are several approaches to identify and prevent carding attacks:
Behavioral Analysis:
Utilize tools that analyze user behavior patterns during online transactions.
Identify anomalies such as rapid, high-frequency purchases, unusual order quantities, or irregular transaction times.
Transaction Monitoring:
Implement real-time transaction monitoring to identify unusual activity.
Set thresholds for the number and frequency of transactions within specific time frames.
Device Fingerprinting:
Utilize device fingerprinting techniques to identify unique characteristics of devices used in transactions.
Detect changes in device parameters, such as sudden switches in IP addresses or device types.
Geolocation Verification:
Verify the geolocation of the user making the transaction compared to the location associated with the credit card.
Flag transactions with significant discrepancies between the user’s claimed location and the actual location.
Address Verification Systems (AVS):
Implement AVS checks to verify that the billing address entered during the purchase matches the one associated with the credit card.
Pay attention to mismatches or incomplete address information.
CAPTCHA Challenges:
Integrate CAPTCHA challenges during checkout to differentiate between human users and automated bots.
Bots often struggle with solving CAPTCHAs, providing an additional layer of verification.
Two-Factor Authentication (2FA):
Implement 2FA to add an extra layer of authentication beyond username and password.
Require users to verify their identity through a secondary method, such as a one-time code sent to their mobile device.
Machine Learning and AI:
Employ machine learning algorithms and artificial intelligence to detect patterns indicative of carding attacks.
Train models on historical data to identify evolving attack strategies.
Blacklist Monitoring:
Maintain and regularly update a blacklist of known fraudulent users, devices, or IP addresses.
Cross-reference incoming transactions against the blacklist to block potentially malicious activity.
Use Behavioural-based Bot Mitigation Solution
During the holiday season, protecting your website from bot attacks is crucial to avoid disruptions for your on-call team. Unchecked bot traffic can harm e-commerce businesses, especially during peak times.
Basic methods like device fingerprinting and IP filtering may not effectively stop modern, distributed attacks.
A robust bot management system is essential. It should instantly identify and block layer 7 DDoS attacks, distinguish between bots and humans in real time and ensure a smooth user experience (UX). The system should operate automatically to save your team time.
Real-time behavioral detection capabilities are crucial to prevent automated attacks like card cracking.
Bot protection solutions like AppTrana use behavior analysis, machine learning, device fingerprinting, and collective bot intelligence for accurate detection with minimal false positives.
Look for providers with a 24/7 support team to handle motivated attackers. A managed service team should monitor bot trends, analyze fraud tools, engage with bot developer communities, and continually improve detection algorithms.
Indusface SOC team offers around-the-clock monitoring during peak events, adjusting to threats, handling bot management tasks, and reviewing events afterward for improvements. This ensures your website stays protected during high-traffic periods.
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