Application-Level Cyberattack Detection with Multistage Classifiers
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Solution Overview
Problem
Existing cybersecurity solutions often trade off between performance and scale or accuracy in detecting cyberattacks, leading to inefficiencies and potential notification fatigue.
Innovation Solution
A multistage cyberattack detection system using a first stage of classifiers for initial analysis and a second stage of machine learning models to enhance accuracy, allowing for real-time analysis of large volumes of requests while reducing false positives.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a single stage classifier is used for cyberattack detection, then the system complexity is low, but the detection accuracy and reliability are insufficient leading to high false positive rates
Solution Approach 1:
The detection system is divided into multiple stages: a first stage classifier processes individual requests quickly, and a second stage classifier analyzes aggregated security event datasets. This segmentation allows each stage to specialize - the first stage filters obvious threats efficiently while the second stage provides deep analysis for complex patterns, resolving the contradiction between accuracy and complexity.
Solution Approach 2:
The system transitions from analyzing single requests in one dimension to analyzing aggregated security event datasets across multiple time periods in another dimension. This dimensional change enables the second stage classifier to detect sophisticated cyberattacks that span multiple requests, improving detection accuracy without proportionally increasing complexity.
2Productivity
If a single stage classifier analyzes all requests thoroughly, then detection accuracy is high, but the processing speed and productivity decrease
Solution Approach 1:
The system segments request processing into two pathways: most requests are quickly evaluated by the first stage classifier maintaining high productivity, while only suspicious requests are passed to the second stage classifier for thorough analysis. This segmentation ensures both fast processing for normal traffic and accurate detection for potential threats.
Solution Approach 2:
Instead of applying full analysis to all requests, the system applies partial analysis (first stage) to all requests and excessive/detailed analysis (second stage) only when necessary. This partial action approach maintains high processing speed for the majority of requests while ensuring accurate detection when needed.
3Reliability
If traditional cybersecurity solutions are used, then the implementation is simple, but notification fatigue occurs due to high false positive rates
Solution Approach 1:
The multi-stage classification system segments the detection process to reduce false positives. The first stage classifier quickly identifies obvious threats, while the second stage classifier provides a second opinion on suspicious requests. This segmented approach significantly reduces false alarms compared to single-stage systems, improving reliability without requiring overly complex infrastructure.
Solution Approach 2:
The system implements feedback mechanisms where the output of the first stage classifier becomes the input for the second stage. Security event datasets are aggregated over time and fed back into the classification process, allowing the system to learn from patterns and continuously improve detection accuracy while maintaining manageable complexity.
Data Source
AI summary
Various embodiments include systems and methods to implement a security platform providing application-level cyberattack detection using multiple stages of classifiers. The security platform may use requests received by a web service to determine training data to train one or more machine learning models. The training data may be determined by instrumenting an application, such as a web service, with a first stage classifier to determine security events indicative of cyberattacks. The security platform may train machine learning models using aggregations of security events over various periods of time. The machine learning models may serve as second stage classifiers for the security platform.