Cloud Fraud Thresholds for Resource Utilization
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Solution Overview
Problem
Cloud computing systems face inefficiencies due to unauthorized and fraudulent users, leading to increased costs and reduced operational efficiency, as existing methods struggle to effectively identify and mitigate such fraudulent activities.
Innovation Solution
Implementing a multi-threshold based method that assigns fraud thresholds linked to key performance indicators (KPIs) to each resource, adjusting these thresholds based on characteristics like capacity, fraud distribution, and subscriber growth rates to identify and manage fraudulent subscribers, thereby ensuring operational efficiency and maintaining KPIs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a cloud provider offers free subscriptions and low-cost computing resources to attract users, then user acquisition and market penetration are improved, but fraudulent and unauthorized usage increases, leading to higher costs and reduced operational efficiency
Solution Approach 1:
The system performs preliminary actions by establishing fraud thresholds and monitoring mechanisms before fraudulent activity occurs. The fraud threshold is pre-configured based on KPIs, and the system continuously monitors resource usage patterns to detect anomalies, enabling preventive fraud detection rather than reactive response.
Solution Approach 2:
The system implements feedback mechanisms by monitoring resource usage patterns and comparing them against established fraud thresholds. When usage patterns deviate from normal behavior, the system provides feedback through threshold violations that trigger fraud detection alerts and potential user suspension, creating a closed-loop fraud prevention system.
2Reliability
If the cloud provider designs with excess capacity to mitigate unauthorized usage, then system reliability and service availability are improved, but the cost of purchasing and maintaining such capacity increases significantly
Solution Approach 1:
The system pre-establishes fraud thresholds based on historical data and KPIs before fraud occurs, enabling proactive detection and prevention of unauthorized usage patterns that would otherwise require excess capacity to accommodate.
Solution Approach 2:
The system continuously monitors resource consumption and compares it against fraud thresholds, providing real-time feedback that enables dynamic adjustment of resource allocation and fraud detection intensity, reducing the need for static excess capacity.
3Object-affected harmful factors
If the cloud provider implements strict fraud detection and user suspension mechanisms, then fraudulent activity is reduced, but authorized users may be incorrectly identified as fraudulent, leading to service disruption
Solution Approach 1:
The system applies different fraud detection stringency to different resources based on their characteristics and fraud risk profiles. Each resource has customized fraud thresholds adjusted to its specific usage patterns and risk characteristics, allowing tailored fraud detection that reduces false positives while maintaining effective fraud prevention.
Solution Approach 2:
The fraud thresholds are dynamic and adapt to changing conditions rather than being static. The system adjusts thresholds based on observed usage patterns, seasonal variations, and evolving fraud tactics, enabling flexible fraud detection that maintains accuracy across different operational contexts and reduces erroneous suspensions.
Data Source
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AI summary
Unauthorized and fraudulent use of a cloud computing system may be reduced or mitigated using a multi-threshold based method to identify fraudulent subscribers of the cloud. The multi-threshold based method may assign a fraud threshold to each resource of the cloud. The fraud thresholds of the multi-threshold based method may be adjusted based on one or more characteristics associated with one or more of the plurality of resources in the cloud. The one or more characteristics may include a capacity percentage associated with the plurality of resources, fraud distribution among the plurality of resources, cost of operation associated with the plurality of resources, anticipated or actual subscriber growth rate associated with the plurality of resources and/or anticipated or actual subscriber fraud risk associated with the plurality of resources.