API Traffic Anomaly Detection via ML Sequence Analysis
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Solution Overview
Problem
Existing systems lack effective methods to monitor and analyze Application Programming Interface (API) traffic to identify and prevent malicious actions, particularly in the context of increasing usage driven by mobile and IoT devices, which can compromise business applications and databases.
Innovation Solution
A system that utilizes a machine learning model to analyze sequences of API calls, calculating consistency scores to detect anomalies and potential malicious activity by comparing actual API call sequences with predicted sequences, and implements remedial actions such as restricting API calls from malicious devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional security measures are used to monitor API traffic, then implementation is simple, but detection precision of malicious actions is insufficient
Solution Approach 1:
The patent introduces an intermediary analysis system that sits between the API traffic source and destination. This intermediary component performs deep sequencing analysis of API calls without disrupting the original communication flow, enabling enhanced detection precision while maintaining system architecture simplicity through non-intrusive monitoring.
Solution Approach 2:
The patent replaces traditional rule-based security mechanisms with machine learning models that analyze API call sequences. These models automatically learn patterns of malicious behavior from training data, substituting manual security configuration with adaptive, data-driven detection that achieves higher precision without proportional increases in operational complexity.
2Reliability
If comprehensive API traffic analysis is performed to identify malicious actions, then security reliability is improved, but processing time increases
Solution Approach 1:
The patent performs preliminary actions by pre-training machine learning models on extensive datasets of normal and malicious API sequences before deployment. During actual monitoring, the pre-trained models can rapidly classify incoming API sequences, achieving high security reliability without the time cost of analyzing every sequence from scratch.
Solution Approach 2:
The patent segments the API traffic analysis into distinct processing stages: sequence extraction, feature representation, model inference, and anomaly classification. This segmentation allows parallel processing of different aspects of API sequences, reducing overall processing time while maintaining comprehensive analysis for reliable malicious action detection.
3Measurement precision
If machine learning models are used to predict API call sequences, then detection precision is improved, but device complexity increases
Solution Approach 1:
The patent extracts the complex machine learning model implementation into a separate, dedicated component that can be independently trained and updated. This extraction allows the main API monitoring system to remain relatively simple while delegating the sophisticated sequence analysis to specialized model modules, reducing overall system implementation complexity.
Solution Approach 2:
The patent uses copying by implementing multiple instances of the same machine learning model architecture for different API endpoints or traffic types. Rather than creating unique complex models for each scenario, standardized model copies can be deployed and configured, reducing implementation complexity through reuse while maintaining detection precision across diverse API sequences.
Data Source
AI summary
In some embodiments, a method includes receiving, at a processor of a server, a first application programming interface (API) call from a client device and providing an indication associated with the first API call as an input to a machine learning model such that the machine learning model identifies a set of parameters associated with a set of likely subsequent API calls. The method can further include receiving a second API call from the client device, identifying the second API call as an anomalous API call based on the second API call not meeting the set of parameters associated with the set of likely subsequent API calls, and sending a signal to perform a remedial action based on the identifying.