ATM Processor Security Agent for Real-Time Threat Detection
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Solution Overview
Problem
Current ATM security systems lack the ability to autonomously assess and respond to security threats in real-time, relying on static rules and lacking self-learning capabilities, which is inefficient and resource-intensive.
Innovation Solution
Implementing an automated system with a rules database, security agent, and algorithm functions within the ATM processor to analyze transitions between states, generate new rules based on data points, and discard or mitigate non-identical transitions, enabling self-learning and threat response with minimal memory footprint.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If legacy security software applications are implemented to provide ATM security, then security protection capability is improved, but memory and resource requirements increase substantially
Solution Approach 1:
The patent extracts only the essential security-related data points from ATM transition flows and stores them in a compact rules database, rather than implementing comprehensive legacy security software. This extraction approach provides security protection while minimizing memory usage by storing only critical state transition rules and data points.
Solution Approach 2:
Instead of using resource-intensive legacy software to provide security, the patent inverts the approach by using a minimal resource footprint system that learns from observed transitions. The ATM processor itself performs security assessment through algorithm functions rather than relying on external heavy software applications.
2Use of energy by moving object
If static rules are used for ATM security, then resource consumption is reduced, but the ability to detect and respond to new threats deteriorates
Solution Approach 1:
The patent implements dynamic security assessment where the ATM processor continuously observes state transitions and updates its understanding of normal versus abnormal behavior. The algorithm function dynamically generates security assessments based on current transition flows rather than relying on fixed static rules, enabling adaptation to new threats while maintaining low resource consumption.
Solution Approach 2:
The system incorporates feedback mechanisms where the ATM processor monitors its own security-related mistakes and learns from them. The security agent extracts data points from transition flows and feeds this information back to the algorithm function, which updates security assessments and prevents repeated mistakes, creating a self-improving security system.
3Adaptability or versatility
If machine learning capabilities are added to enable self-learning security assessment, then adaptability to new threats is improved, but device complexity increases
Solution Approach 1:
The patent segments the security assessment function into distinct modular components: a security agent for extracting data points, an algorithm function for generating security assessments, and a rules database for storing learned patterns. This segmentation enables machine learning capabilities while managing complexity through clear functional separation and modular architecture.
Solution Approach 2:
The ATM processor performs its own security assessment without requiring external security systems. The algorithm function within the ATM processor autonomously evaluates security risks by analyzing its own transition flows, enabling self-learning while avoiding the complexity of external machine learning systems.
4Loss of information
If comprehensive security monitoring is implemented to understand security issues, then security awareness is improved, but processing overhead increases
Solution Approach 1:
The patent implements partial monitoring by focusing only on security-critical state transitions rather than comprehensively monitoring all ATM operations. The security agent extracts only relevant data points from transition flows, providing sufficient security awareness while minimizing processing overhead by avoiding excessive monitoring of non-critical operations.
Data Source
AI summary
Systems and methods involve a database function of an ATM processor on which rules database records for positive transition flows of ATM hardware or software activities are stored, a security agent function of the ATM processor that extracts data points from a transition flow for every succeeding ATM activity, and an algorithm function of the ATM processor that generates a rules database record for the transition flows for succeeding ATM activity based on the extracted data points and discards any generated rules database record that is identical to a rules database record already stored on the rules database function. A discovery phase of the algorithm function stores new rules database records, rules database function, and a protection phase of the algorithm function selects a risk protocol, when a generated record is not identical to a record already stored.