ML Security Event Detection for Card Testing Attacks
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Solution Overview
Problem
Existing systems struggle to effectively detect card testing attacks, a type of card not present (CNP) fraud, due to the challenges posed by rapid transaction volume, use of bots and VPNs, and difficulty in differentiating between legitimate and fraudulent activity, especially in online transactions.
Innovation Solution
Implementing a machine-learning (ML) based system with two ML models to transform and classify transaction data, allowing real-time detection of card testing attacks by analyzing patterns and anomalies, and adapting to new fraud patterns through continuous re-training.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional fraud detection algorithms are used to monitor transactions, then security measures are implemented, but the system cannot effectively detect card testing attacks due to rapid transaction volume and use of bots/VPNs
Solution Approach 1:
The patent replaces traditional rule-based fraud detection algorithms with machine learning models (specifically transformer-based models) that can process transaction data sequentially and identify complex fraud patterns. The ML model processes transactions in a sequential manner, maintaining context across multiple transactions to detect card testing attacks that traditional systems miss, thereby improving detection accuracy without sacrificing processing speed.
Solution Approach 2:
The patent changes the detection parameters from static rules to dynamic machine learning models that continuously adapt to new fraud patterns. The transformer model processes transactions through multiple attention heads that analyze different aspects of transaction data, enabling the system to detect sophisticated fraud techniques like card testing attacks while maintaining high processing throughput.
2Reliability
If machine learning models are used to detect fraud patterns, then detection capability is improved, but system complexity increases
Solution Approach 1:
The patent segments the fraud detection system into distinct components: a transformer-based ML model for pattern recognition, a sequential processing mechanism for context maintenance, and a classification output system. This segmentation allows the complex ML model to be integrated systematically without overwhelming the entire system, improving fraud detection capability while managing complexity through modular architecture.
3Reliability
If transaction data is processed to detect card testing attacks, then security is enhanced, but memory requirements increase
Solution Approach 1:
The patent replaces traditional batch processing methods with a sequential processing approach using transformer models. This substitution allows the system to process transactions one at a time while maintaining context through the model's internal state, significantly reducing memory requirements compared to loading entire transaction histories into memory for analysis, while still achieving enhanced security through sophisticated pattern recognition.
Data Source
AI summary
Systems and methods provide for security event detection using machine learning. Event data items are processed using a first machine-learning model to generate an encoding for each corresponding event data item. Each encoding is processed using a second machine learning model to generate a classification indicating whether the corresponding event is fraudulent. A security event is determined based on some of the generated classifications. In response to detecting the security event, an event processing rate is adjusted.