Network Characterization System for Rogue Access Point Detection
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Solution Overview
Problem
The complexity of modern computing environments with numerous broadband networks poses security risks and management difficulties, as users often face challenges in seamlessly connecting to networks due to the presence of rogue access points and multiple connection clients, leading to resource consumption and user frustration.
Innovation Solution
A network characterization system that includes a detection unit to identify network indicators, a classification unit to categorize networks as trusted, untrusted, or semi-trusted based on provided attributes, and a characterization unit to verify the accuracy of these classifications, ensuring secure and seamless connections by leveraging additional information beyond network provider identifiers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If users use multiple connection clients to connect to multiple networks, then network connectivity is improved, but system resource consumption increases and user confusion increases
Solution Approach 1:
The patent implements a universal connection client that can handle multiple network types (WiFi, Bluetooth, cellular, wired Ethernet) through a single unified interface. The system automatically detects available networks, classifies them by trust level, and manages connections across different network technologies without requiring separate clients for each network type, thereby reducing system resource consumption while maintaining versatile connectivity.
2Adaptability or versatility
If users manually manage multiple connection clients, then network access flexibility is improved, but ease of operation deteriorates due to user confusion and frustration
Solution Approach 1:
The system implements automatic network discovery, classification, and connection management without requiring manual user intervention. The connection client automatically detects available networks, classifies them by trust level based on various indicators, prioritizes trusted networks, and establishes connections autonomously. The system also automatically re-evaluates network trust levels and switches connections when needed, eliminating user confusion while preserving access flexibility.
Solution Approach 2:
The system performs preliminary network classification and trust assessment before users need to connect. By proactively evaluating networks and establishing a hierarchy of trusted versus untrusted networks in advance, the system prepares connection options ready for user selection or automatic connection, reducing the cognitive load and decision-making complexity for users at the moment of connection need.
3Speed
If the system classifies networks based on provider identifiers, then connection speed is improved, but reliability deteriorates due to rogue access points
Solution Approach 1:
The system implements continuous feedback loops for network evaluation. After initially classifying networks based on provider identifiers for fast connection, the system continuously monitors network behavior, checks for expected attributes and services, and re-evaluates trust levels based on actual network performance and characteristics. This feedback mechanism allows the system to quickly connect to suspected rogue networks while automatically detecting and disconnecting from malicious networks, maintaining both speed and reliability.
Solution Approach 2:
The system performs preliminary classification based on provider identifiers to enable fast initial connection assessment, but immediately follows up with verification steps including checking for expected network attributes, services, and behaviors. This preliminary action followed by verification allows the system to quickly identify potential networks while maintaining security through subsequent validation, preventing connection to rogue access points that mimic legitimate networks.
Data Source
AI summary
In one embodiment the method includes detecting a network indicator associated with a network, the network provided by a network provider. In one embodiment, after the detecting of the network indicator, the method includes obtaining from the network provider an actual attribute associated with the network. The method may also include determining, based on stored network information, whether the actual attribute matches an expected attribute for the network. If the actual attribute matches the expected attribute, the method may classify the network into one of a plurality of network classes.