Application Interface Endpoint Detection with Probabilistic Classification
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Solution Overview
Problem
Existing methods struggle to efficiently and automatically identify application interface endpoints in complex networks due to the lack of binding guidelines and varying responses, making manual verification time-consuming and rule-based searches imprecise.
Innovation Solution
A method involving an application interface generator, collector, and classifier, utilizing machine learning algorithms to generate, collect, and evaluate network addresses to determine the probability of an endpoint, allowing targeted and resource-efficient identification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual verification of network addresses is performed, then detection accuracy is improved, but time consumption increases significantly
Solution Approach 1:
The patent introduces an automated detection system that acts as an intermediary between manual verification and network addresses. The system uses machine learning models trained on response patterns to automatically evaluate whether a network address is an application interface endpoint, replacing manual verification while maintaining high accuracy through pattern recognition rather than direct human inspection of each address.
Solution Approach 2:
The system enables self-service detection by training machine learning models on collected response data from network queries. The models automatically improve their detection capability through feedback from actual network responses, eliminating the need for continuous manual verification and allowing the system to autonomously identify application interface endpoints with high accuracy.
2Extent of automation
If rule-based automatic search is implemented, then automation level is improved, but detection precision deteriorates due to varying responses
Solution Approach 1:
The patent transitions from static rule-based detection to dynamic machine learning-based detection. The system adapts to varying responses by training models on actual network response data, allowing detection precision to improve dynamically as the system learns from diverse response patterns rather than relying on fixed rules that cannot accommodate variability.
Solution Approach 2:
The system changes the detection parameters from fixed rule thresholds to probabilistic assessments generated by machine learning models. By analyzing response patterns and assigning probabilities rather than applying binary rules, the system maintains high automation while achieving accurate detection despite varying responses from different endpoints.
3Reliability
If exhaustive network scanning is performed, then completeness of detection is improved, but resource consumption increases
Solution Approach 1:
The patent performs preliminary actions by training machine learning models on collected response data before conducting full detection. The models learn from a subset of network responses and can then accurately predict whether other addresses are endpoints without requiring exhaustive scanning, achieving high completeness with reduced resource consumption through pre-trained pattern recognition.
Solution Approach 2:
The system uses partial action by querying only sufficient sample addresses to train the machine learning models effectively. Rather than scanning the entire network exhaustively, the system performs partial scanning to collect training data, then uses the trained models to infer the status of remaining addresses, achieving comprehensive detection with minimal resource expenditure.
Data Source
AI summary
The invention relates to a method for detecting an application interface endpoint, wherein the network address of a potential application interface endpoint is generated, wherein an attempt is made to establish a connection to the generated network address of the potential application interface endpoint via a network protocol, and wherein a result of the connection establishment attempt is evaluated to determine the probability that the generated network address is an application interface endpoint. Furthermore, the invention relates to a system for carrying out the method, comprising an application interface generator, a collector, and a classifier.