API Poller Tuning for Low-Latency Cybersecurity Event Ingestion
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Solution Overview
Problem
Existing cybersecurity systems face challenges in scaling threat detection capabilities efficiently to handle the increasing volume of security threats without causing technical inefficiencies, leading to delayed detection and response.
Innovation Solution
A computer-implemented method that dynamically computes an optimal polling frequency control value based on historical polling performance data, partitions the time span into sub-intervals, and transmits distinct network requests to retrieve raw event data, reducing the number of network requests and optimizing memory and processing resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If the cybersecurity service scales up threat detection capabilities to handle increasing volume of security threats, then the detection coverage and capacity improve, but technical inefficiencies increase leading to delayed detection and response
Solution Approach 1:
The system dynamically adjusts the polling frequency for different API endpoints based on historical performance data, event types, and system load conditions. Instead of using static polling intervals, the service computes optimal polling frequencies in real-time, allowing it to adapt to changing threat volumes and system states, thereby maintaining efficient detection without unnecessary delays
Solution Approach 2:
The system changes the polling frequency parameter dynamically based on computed optimal values. By adjusting this critical parameter according to historical performance data and current system state, the service optimizes data retrieval efficiency, reducing detection latency while handling increased threat volumes effectively
2Speed
If the service increases polling frequency to retrieve more event data, then detection speed improves, but network request volume and resource consumption increase
Solution Approach 1:
The system optimizes the polling frequency parameter by computing optimal values based on historical performance data, event types, and system conditions. This dynamic parameter adjustment ensures fast data ingestion when threats are detected while reducing polling frequency during normal conditions, thereby balancing speed with resource efficiency
Solution Approach 2:
The system uses historical polling performance data as feedback to continuously optimize polling frequencies. By analyzing past performance metrics and adjusting future polling behavior accordingly, the service achieves efficient resource utilization while maintaining rapid detection capabilities when needed
Data Source
AI summary
A system, method, and computer-program product includes generating a polling task that is configured to retrieve raw event data of a subscriber that occurred during a target time span from a third-party security service, computing an optimal polling frequency control value for the polling task in response to generating the polling task, partitioning the target time span into a plurality of distinct sub-intervals of time based on the optimal polling frequency control value computed for the polling task, automatically transmitting, to an application programming interface endpoint of the third-party security service, a plurality of distinct network requests to optimally perform the polling task, receiving the raw event data of the subscriber from the third-party security service that occurred during the target time span in response to transmitting the plurality of distinct network request, and generating one or more security alerts in response to processing the raw event data.