Mitigating Carding Attacks for a US-Based Leading Jewellery Company

Key Challenges:  

• The customer faced persistent and frequent carding attacks (also known as credit card stuffing/card verification attacks) on their application.  

• A carding attack is an attack where attackers  use stolen/fake credit card information  and try to make online purchases.  

• Similarly, in this situation, the attacker(s) utilized multiple bots to enter fake/stolen credit card details, randomly generated fake email addresses, and tried to make a purchase. 

• A worrisome concern for the customer was that by using this bot automation, the attacker could even place around  15 fake orders  from their site by following this process. 

• While the orders didn’t go through, the jeweller was facing the risk of getting blacklisted by 3rd party payment providers for the volume of fake orders 

• In the event of a successful blacklisting, the jeweller faced potential losses of hundreds and thousands of dollars per day  

Strategy & Recommended Solution: 

• The customer contacted the Indusface team and was able to deploy the AppTrana solution within 60 minutes of the request  

• The AI engine of AppTrana was immediately activated to find any anomalies in the incoming traffic  

• Within a few minutes, the AI engine identified a unique bot pattern in the traffic that appeared to mimic human behaviour (and had unique IDs and cookies associated with them). This indicated that the bots were not generated by standard automated tools but were browser-based bots designed exclusively by the hackers.  

• These bots executed all processes and functions just like a normal user, from selecting a product to making a payment, making it difficult to differentiate between normal users and hackers.    

• The attacker even made sure to use different IP addresses from various countries during each attempt, further complicating detection efforts. 

• The AI engine tracked user behavior and identified patterns of the bot attacks based on historical data, highlighting randomizations in parameters such as Bank Identification Number (BIN) and credit card details.  

• Following the AI engine’s recommendations, the managed services team deployed custom rules to monitor and block any users attempting to alter standard parameters linked to the carding attack. 

• It was ensured that any parameter deviations made by the user for up to a specific number of attempts within a specific time frame were logged, and all the attempts exceeding those attempts were blocked.  

• AppTrana’s AI engine ensured that IP addresses (series) used for the carding attacks were blocked to prevent further access for a defined period 

• Furthermore, if the user/attacker belonged to a geolocation where the customer had no scope of doing business, then the AI capability of the AppTrana WAAP blocked such requests immediately. 

• All the above rules were deployed within a 48-hour time frame, while working with the customer to continuously remove false positives  

• In spite of the reduction in attacks, Indusface’s 24*7 managed services constantly monitored the AI-suggested tuning recommendations and adjusted the defence mechanisms on an ongoing basis 

• Since the deployment of the AppTrana WAAP, the customer has punched  zero fake orders due to carding attacks  over the past year. 

Results:  

• Successful mitigation of carding attacks within 48 hours of request  

• Significant reduction in fraudulent transactions and zero fake orders  

• Regained control over the brand reputation  

• Efficient and quick response to prevent disruptions caused by carding attacks