Tracking Payment Card Data Flow Using Virtual Machine State Introspection

Credit and debit card payment processing systems are key elements in financial transactions. Negligence in securing these systems makes them vulnerable to hacking attacks, which may lead to significant monetary losses for both merchants and the financial organizations. To reduce this risk, mandatory security compliance regulations, such as the Payment Card Industry Data Security Standard (PCI DSS), were developed and adopted by the industry. A key pre-requisite of the PCI DSS compliance process is the ability to identify the components of the payment systems directly involved with the card data (i.e. process, transmit, or store). However, existing data flow tracking tools cannot fully automate the process of identifying system components that touch card data, because they either can not examine encrypted communications or they use an instrumentation-based approach and thus require a priori detailed knowledge of the payment card processing systems.

We describe the implementation and evaluation of a novel tool to identify the card data flow in commercial payment card processing systems running on virtualized servers. The tool performs real-time monitoring of network communications between virtual machines and inspects the memory of the communicating processes for unencrypted card data. Our implementation does not require instrumentation of application binaries and can accurately identify the system components involved in card data flow even when the communications among system components are encrypted. Effectiveness of this tool is demonstrated through its successful discovery of the card data flow of several open- and closed-source payment card processing applications.

Author(s):

Jennia Hizver    
Stony Brook University
United States

Tzi-cker Chiueh    
Stony Brook University
United States