AI Domain Derivation from IP Addresses via OSINT and Ensemble Models
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Solution Overview
Problem
Current technologies lack a method to efficiently match a domain with an Internet Protocol (IP) address, which is essential for network management tasks such as detecting phishing sites, analyzing security logs, and limiting server access.
Innovation Solution
An electronic device equipped with a processor and memory, utilizing a machine-learning scheme and multiple AI models (such as RandomForest, XGBoost, SVM, LightGBM, CatBoost, Logistic Regression, and Lasso) to derive domain information from Hypertext Markup Language (HTML) sources associated with target IP addresses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple AI models are used to derive domain information from HTML sources, then the accuracy and reliability of domain matching is improved, but the device complexity and processing time increase
Solution Approach 1:
The system divides the domain matching task into multiple independent AI models, each specialized in processing specific types of HTML source information. Each model operates independently and contributes to the overall accuracy through ensemble voting, resolving the contradiction by organizing complexity into manageable functional segments rather than a monolithic complex system
Solution Approach 2:
Multiple AI models are combined through a unified processing architecture where their outputs are aggregated to produce the final domain matching result. The models are merged in a coordinated manner with shared data structures and a common evaluation framework, achieving high reliability while controlling overall system complexity through structured integration
2Measurement precision
If multiple AI models process HTML sources to derive domain information, then the measurement precision of domain matching is improved, but the processing time and computational resources increase
Solution Approach 1:
The system performs preliminary processing of HTML sources by extracting and pre-processing data before it enters the AI models. Common features are identified and prepared in advance, reducing the computational burden during model processing and thereby decreasing overall processing time while maintaining high measurement precision through the ensemble of models
Solution Approach 2:
The system applies partial processing strategies where not all AI models process all input data equally. Based on the characteristics of the HTML source and the specific matching task, the system selectively activates appropriate models, avoiding unnecessary computational overhead while ensuring sufficient processing to achieve the required domain detection precision
Data Source
AI summary
Provided are an electronic device for deriving a domain connected to the IP address based on Open Source INTelligence (OSINT) information and for deriving the domain connected to the IP address based on an artificial intelligence (AI) model. and a method for the same.