Neural Network API Risk Assessment
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Solution Overview
Problem
Web application programming interfaces (APIs) are vulnerable to cyber-attacks due to self-documentation of implementation and internal structure information, weak authentication, lack of encryption, business logic flaws, and insecure endpoints, leading to resource wastage in detecting, responding to, and recovering from attacks.
Innovation Solution
A risk assessment platform utilizing a neural network model to extract content, path data, and query data from API requests, generate sequence-embedded events, train a neural network model, and calculate anomaly scores to determine risk, blocking or permitting requests based on thresholds.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional API security methods are used, then implementation simplicity is maintained, but detection precision and response effectiveness deteriorate due to inability to identify sophisticated attacks
Solution Approach 1:
The patent introduces an intermediary system comprising event extraction module, sequence embedding module, and neural network model that mediates between API requests and security analysis. This intermediary layer transforms raw API requests into structured events, embeds temporal sequences, and applies trained neural networks to detect anomalies, thereby achieving high detection precision without requiring direct complex integration into existing API infrastructure
Solution Approach 2:
The security system is segmented into distinct functional modules: event extraction module that parses API requests, sequence embedding module that temporalizes events, training module that prepares neural network models, and inference module that performs anomaly detection. This segmentation allows each component to be optimized independently and deployed flexibly, reducing overall system complexity while maintaining high detection capabilities
2Reliability
If comprehensive security monitoring is implemented, then reliability improves, but loss of time in detection and response increases due to extensive analysis requirements
Solution Approach 1:
The system performs preliminary actions by pre-training neural network models on historical API request data before deployment. The training module uses sequence-embedded events from historical data to teach the model normal and anomalous patterns in advance. During runtime, the pre-trained model can quickly evaluate new requests without extensive real-time analysis, thereby maintaining high reliability while minimizing detection time
Solution Approach 2:
The patent replaces traditional mechanical rule-based security analysis with a neural network-based intelligent system. Instead of manually configuring security rules and patterns, the system uses machine learning models that automatically learn from data and adapt to new threats. This substitution enables parallel processing of multiple security checks, significantly reducing detection time while improving reliability through data-driven insights
3Productivity
If manual security analysis is used, then resource consumption is low, but productivity in detecting and responding to attacks deteriorates
Solution Approach 1:
The system implements self-service by automatically training neural network models using historical API request data without requiring continuous manual intervention. The training module autonomously processes historical events, extracts patterns, and updates models. During operation, the system self-adjusts by continuously learning from new data, enabling high detection productivity while optimizing resource usage through automated model refinement rather than manual analysis
4Ease of operation
If weak authentication and lack of encryption are present, then ease of operation is improved, but object-generated harmful factors increase due to security vulnerabilities
Solution Approach 1:
The patent introduces an intermediary security layer that sits between API operations and underlying systems. This intermediary monitors and analyzes API requests without requiring changes to existing authentication or encryption implementations. By embedding sequence information and applying neural network analysis, it detects vulnerable operations and potential attacks, thereby maintaining ease of operation while reducing harmful factors through intelligent oversight rather than forcing complex security protocol changes
Data Source
AI summary
A device may extract content, path data, and query data from API requests, and may identify events based on the extracted data. The device may combine a sequence of the events with the events to generate sequence-embedded events, and may train a neural network model with the sequence-embedded events to generate a trained neural network model. The device may receive an API request, may extract new content, new path data, and new query data from the API request, and may identify new events based on the newly extracted data. The device may process the new events, with the trained neural network model, to generate predicted events, and may calculate an anomaly score based on the predicted events. The device may compare the anomaly score with a threshold to determine a risk for the API request, and may cause the API request to be blocked or permitted based on the risk.