Bot Prevention Velocity Framework for Real-Time Request Detection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional techniques fail to efficiently identify and prevent bot activity in online retail environments, leading to high-demand items being purchased by bots instead of genuine customers during special sale events.
Innovation Solution
A bot prevention velocity framework that monitors and analyzes transaction data to detect bot activity by measuring the speed and patterns of requests, using configurable policy rules to identify and mitigate bot behavior.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional bot detection techniques are used, then bot activity can be identified to some extent, but bot activity is not accurately detected and prevented in time, allowing bots to purchase limited inventory before genuine customers
Solution Approach 1:
The system pre-configures velocity rules with thresholds and actions before bot activity occurs. These rules are dynamically configured based on historical data and patterns, allowing the system to immediately detect and respond to bot behavior without delay for rule creation or analysis during the purchase event.
Solution Approach 2:
The system continuously monitors velocity data from requests and provides real-time feedback by comparing actual request rates against configured thresholds. This feedback mechanism enables dynamic adjustment and immediate detection of bot activity patterns, improving both detection accuracy and response time.
2Reliability
If velocity monitoring is implemented to detect bot requests, then bot detection capability is improved, but system complexity increases due to the need to monitor and analyze velocity data patterns
Solution Approach 1:
The system segments bot detection into distinct components: velocity data collection from requests, separate rule configuration for thresholds and actions, and dedicated evaluation logic that matches observed velocity against rules. This modular segmentation improves reliability while managing system complexity through clear separation of concerns.
Solution Approach 2:
The velocity rule configuration acts as an intermediary layer between raw velocity data and detection decisions. By introducing configurable thresholds and rules as a mediator, the system achieves reliable bot detection without hardcoding complex detection logic, thereby managing system complexity through flexible, configurable parameters.
3Adaptability or versatility
If dynamic policy rule configuration is used to adapt to different bot behaviors, then adaptability to various bot patterns is improved, but the complexity of managing and configuring policy rules increases
Solution Approach 1:
The system implements dynamic policy rules that can be configured with different velocity thresholds, time windows, and actions to adapt to various bot behaviors. Rules can be dynamically adjusted based on observed patterns, allowing the system to respond to evolving bot strategies while maintaining manageable complexity through a standardized rule structure.
Data Source
AI summary
The embodiments relate to a system, a computer-implemented method, and a computer program product for performing bot detection using a velocity framework. For example, embodiments include a policy decision engine that can receive requests from a source, wherein each of the requests comprise velocity data including one or more attributes. The policy decision engine can monitor an occurrence of the velocity data in each request, and determine a velocity data rate for the velocity data in each request. Further, the policy decision engine can determine whether the request is a bot request based at least in part on the determined velocity data rate, and transmit a notification to the source of the request based at least in part on the determination of the bot request, wherein the notification indicates whether a bot request has been identified in the request.