Network Traffic Control via Address Classification
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Solution Overview
Problem
Current network traffic control systems face challenges in effectively classifying and managing communication addresses, leading to inefficiencies in service level determination, spam filtering, and congestion management, particularly when dealing with unverified or malicious communication sources.
Innovation Solution
A network traffic control system that employs a traffic service-level classifier using machine learning algorithms and various combinatorial algorithms to classify communication addresses, trigger reactive address verification, and adjust service levels, while also utilizing spam control algorithms and congestion controllers to prioritize messages and manage spam.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional traffic control systems are used to manage communication addresses, then device complexity is reduced, but service level determination accuracy deteriorates and spam filtering effectiveness decreases
Solution Approach 1:
The system segments communication addresses into different classes (restricted, verified, trusted) based on verification status and behavior patterns. This segmentation enables differentiated service levels and spam filtering strategies for each class, improving accuracy without requiring a complete system redesign.
Solution Approach 2:
The system performs preliminary address verification and classification before traffic routing decisions are made. By pre-verifying addresses and assigning them to appropriate classes, the system establishes accurate service level determinations in advance, reducing the need for complex real-time analysis.
2Object-generated harmful factors
If aggressive spam filtering is applied to all communication addresses, then spam reduction improves, but loss of legitimate messages increases and network efficiency deteriorates
Solution Approach 1:
The system applies different filtering aggressiveness levels to different address classes locally. Restricted addresses receive aggressive filtering, verified addresses receive moderate filtering, and trusted addresses receive minimal or no filtering. This localized approach reduces spam from malicious sources while preserving legitimate traffic from verified and trusted senders, maintaining network efficiency.
Solution Approach 2:
The system uses feedback from message routing outcomes and address verification results to dynamically adjust filtering strategies. Addresses that generate legitimate traffic have their filtering reduced, while addresses generating spam maintain aggressive filtering, optimizing the balance between spam reduction and network efficiency over time.
3Reliability
If unverified communication addresses are treated with default restrictions, then network security improves, but loss of legitimate communication increases
Solution Approach 1:
The system performs preliminary verification attempts and behavior analysis on unverified addresses before applying default restrictions. Addresses that pass initial verification or exhibit legitimate communication patterns are assigned to the verified class with reduced restrictions, while only those failing verification maintain restricted status, reducing legitimate communication loss while maintaining security.
Solution Approach 2:
The system dynamically adjusts address classification and service levels based on ongoing verification status and communication behavior. Addresses can transition between restricted, verified, and trusted classes as their verification status changes, allowing the system to maintain security for unverified addresses while reducing restrictions for those proven legitimate, thereby minimizing loss of legitimate communication.
Data Source
AI summary
An example method of traffic control in a communication network includes the operations of: receiving, by a processing device, a message associated with a communication address of a party originating the message; determining a class of service associated with the communication address; identifying a network resource locator contained in the message; responsive to detecting, in a chain of redirects associated with the network resource locator, an identifier of a related entity represented by one of: a trusted entity or a known bad actor, adjusting the class of service based on the related entity; selecting one or more links having their respective reliabilities corresponding to the adjusted class of service; and forwarding the message via the one or more links.