Context-Aware Password Security for Device and Network Credentials
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Solution Overview
Problem
Existing password management systems struggle to balance security and usability, particularly in large-scale environments, as they often rely on simple or reused passwords, and traditional strength evaluation methods fail to consider contextual relationships between passwords and network or device-specific information.
Innovation Solution
A method and computer program product that determine password security by assessing proximity to contextual information using machine learning, neural networks, fuzzy measurement methods, entropy measures, and compression algorithms, enabling adaptive and context-aware security measures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional password strength evaluation methods are used (focusing on length, character diversity, and resistance to dictionary attacks), then password complexity requirements are met, but contextual vulnerabilities related to device or network information are not detected
Solution Approach 1:
The patent extends password evaluation from traditional one-dimensional metrics (length, character diversity) to multi-dimensional assessment by incorporating contextual information from device identifiers, network data, and user profiles. This dimensional expansion enables detection of contextual vulnerabilities that traditional methods miss, resolving the contradiction between meeting complexity requirements and detecting contextual weaknesses.
Solution Approach 2:
The evaluation system is segmented into multiple independent analysis modules: traditional strength evaluation, contextual information extraction, proximity calculation, and vulnerability assessment. Each module handles specific aspects separately, allowing comprehensive security evaluation without compromising measurement precision in any single dimension.
2Ease of operation
If simple or reused passwords are used to improve usability and ease of remembering, then user convenience increases, but security against unauthorized access decreases
Solution Approach 1:
The system provides immediate feedback to users about the security weaknesses of their passwords by calculating proximity to contextual information. This feedback mechanism allows users to understand why their password is vulnerable and make informed adjustments, balancing usability with security without requiring users to memorize complex passwords.
Solution Approach 2:
The patent dynamically adjusts security requirements based on the calculated proximity between passwords and contextual information. Instead of applying fixed complexity rules, the system modifies evaluation parameters according to the specific contextual risks identified, allowing flexible security enforcement that adapts to each password's unique vulnerability profile.
3Ease of operation
If passwords are derived from device or user information to improve memorability, then usability increases, but vulnerability to deduction attacks increases
Solution Approach 1:
The patent replaces manual password creation processes with automated computational methods that calculate proximity between passwords and contextual information using algorithms. This substitution of mechanical user judgment with computational analysis enables objective detection of deduction vulnerabilities that users cannot identify themselves.
Solution Approach 2:
The system introduces an intermediary evaluation layer between password creation and security assessment. This intermediary calculates the proximity metric that mediates between the user's intent to create memorable passwords and the security risk of deduction attacks, providing a bridge that allows both goals to be considered in the final security determination.
Data Source
Figure 1
AI summary
Method, computer program product and network control device for determining security of a password-using device or in a password-using network. A method for determining security in a password-using network is provided. The method includes determining the proximity of a password of the password-using network to contextual information of the network or constituents of the network. The method may further comprise determining security measures dependent on the determined proximity. The proximity determination may involve setting or evaluating the proximity, while determining security may involve setting or evaluating the security. Security enhancing measures may be chosen based on the determined proximity. These measures can include providing user feedback about password proximity and receiving password updates, or prohibiting or granting network access. The method may attribute the chosen password to a particular device or group of devices within the network. Proximity evaluation can utilize machine learning, fuzzy measurement methods, entropy measures, or compression algorithms.