DNS Traffic Redirection via Learning Module
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Users often encounter issues with misspelled or non-existent domain names, malicious websites, and unintended content while browsing the internet, leading to potential harm or frustration, with existing technologies failing to effectively redirect or filter such traffic effectively.
Innovation Solution
An Internet improvement platform that directs and filters DNS server traffic using a learning module to analyze user interactions and global data, redirecting queries to appropriate sites, blocking malicious content, and enhancing user experience through modules for non-existent domain, malicious domain, and user access redirection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If DNS queries are redirected based on simple rules, then implementation is easy, but accuracy in identifying malicious or non-existent domains is low
Solution Approach 1:
The system implements feedback mechanisms where user interactions with redirected pages are monitored and fed back into the learning module. This feedback loop enables the system to refine its domain identification accuracy over time by learning from actual user behavior patterns, resolving the contradiction between simple implementation and high accuracy.
Solution Approach 2:
The learning module operates autonomously to improve the system's capabilities without requiring manual retraining. It self-adjusts its filtering rules based on observed user interactions and global data patterns, enabling the system to maintain high accuracy while keeping the base implementation simple.
2Measurement precision
If the system monitors all user interactions and global data, then learning capability and filtering accuracy improve, but system complexity and data processing requirements increase
Solution Approach 1:
The system extracts only the most relevant features from the vast amount of collected data about user interactions and global DNS patterns. By focusing on key indicators such as user behavior patterns and domain request characteristics, the system achieves high filtering accuracy without processing every detail, thus managing complexity.
Solution Approach 2:
The system segments the complex data processing task into separate functional modules: data collection, feature extraction, learning model processing, and filtering decision. This segmentation allows each module to handle specific aspects of data processing independently, reducing overall system complexity while maintaining high accuracy.
3Ease of operation
If the system redirects all queries to search results, then user experience improves for non-existent domains, but legitimate traffic may be disrupted
Solution Approach 1:
The system applies different redirection strategies to different types of queries based on local characteristics. Instead of uniform redirection, it analyzes individual query patterns, user histories, and domain characteristics to determine the most appropriate response, thereby improving user experience for erroneous queries while preserving legitimate traffic flow.
Solution Approach 2:
The redirection policy is dynamic rather than static. The learning module continuously adapts redirection decisions based on real-time observations of user behavior and global data patterns. This dynamic adjustment allows the system to respond appropriately to different query contexts, maintaining both user experience and traffic reliability.
Data Source
AI summary
Redirecting DNS traffic includes receiving, at an Internet improvement platform, a DNS query issued from an Internet application running on a computing device. The Internet navigation platform determines an appropriate response to the DNS query. The response is then executed. Characteristics relative to the query and to the result of a served page may be recorded and later referenced by the Internet improvement platform.