Adaptable Spike Detection Algorithm for Dynamic Resource Constraints
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Solution Overview
Problem
Existing Intrusion Detection Systems (IDS) face challenges in timely and accurate identification of breaches or vulnerabilities due to excessive resource consumption by spike detection tools, leading to performance degradation and delayed critical alerts.
Innovation Solution
The system adapts a spike detection algorithm by monitoring constraint metrics in the computing environment, generating an adapted detection algorithm to reduce resource utilization, and executing it to detect abnormal events, thereby optimizing resource usage and improving alert timeliness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a spike detection tool is implemented in an IDS, then anomaly detection capability is improved, but resource consumption increases causing performance degradation
Solution Approach 1:
The patent implements dynamic adaptation of the spike detection algorithm based on real-time constraint metrics. The system monitors resource consumption and automatically adjusts detection parameters, algorithm complexity, and monitoring intensity to match available system resources, resolving the contradiction between maintaining detection capability and preserving system performance.
Solution Approach 2:
The system changes detection parameters dynamically based on constraint metrics. When resource constraints are detected, the system modifies algorithm parameters such as detection sensitivity, sampling frequency, and processing intensity to reduce resource consumption while maintaining adequate anomaly detection capability.
2Reliability
If a spike detection tool is implemented in an IDS, then anomaly detection capability is improved, but processing time increases causing delayed alerts
Solution Approach 1:
The system dynamically adjusts detection intensity and processing speed based on real-time constraints. When system resources are constrained, the algorithm automatically reduces processing depth and updates detection thresholds to enable faster alert generation without sacrificing critical anomaly detection capability.
Solution Approach 2:
The system employs periodic monitoring and adaptive updates of detection algorithms. Instead of continuous heavy processing, the system performs periodic constraint metric collection and algorithm adaptation cycles, reducing processing time while maintaining detection effectiveness.
3Productivity
If resource consumption is reduced by simplifying the detection algorithm, then system performance is improved, but detection accuracy may deteriorate
Solution Approach 1:
The system changes algorithm parameters adaptively based on constraint metrics. When resources are abundant, more complex algorithms with higher accuracy are deployed. When resources are constrained, the system switches to simplified algorithms with adjusted parameters that maintain adequate accuracy for the available computational power.
Solution Approach 2:
The system applies partial detection algorithms that focus on the most critical detection tasks rather than attempting comprehensive analysis. This selective approach maintains sufficient detection accuracy for security purposes while significantly reducing resource consumption.
Data Source
AI summary
Methods, systems, apparatuses, and computer-readable storage mediums are described for adapting a spike detection algorithm. A first detection algorithm that monitors a first set of events in a computing environment is executed. A set of constraint metrics in the computing environment are monitored. Based on the monitored set of constraint metrics, a second detection algorithm is generated. The second detection algorithm is an adapted version of the first detection algorithm and is configured to monitor a second set of events in the computing environment. The second detection algorithm is executed, and a remediation action is performed in response to an abnormal event detected in the computing environment by the second detection algorithm.