User Security Awareness Management via Psychological Trait Extraction
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Solution Overview
Problem
Conventional security management systems fail to effectively warn users with lower levels of security awareness, as they do not consider psychological traits in issuing security warnings, leading to increased vulnerability to cracking attempts.
Innovation Solution
A management method that extracts psychological and behavioral characteristics of users, calculates a psychological characteristic value, and distributes targeted measures to users whose values exceed a predetermined threshold, taking into account their psychological traits and past behaviors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If security warnings are issued based on conventional log analysis of user behavior, then system security monitoring is maintained, but users with lower security awareness cannot be effectively identified or warned
Solution Approach 1:
The system segments user characteristics into distinct components: psychological characteristics (extracted from questionnaire responses) and behavioral characteristics (extracted from log data). This segmentation allows for targeted analysis of security awareness without requiring a monolithic complex system, as each segment can be processed independently and then integrated.
Solution Approach 2:
The system introduces psychological characteristic values as an intermediary metric that bridges the gap between observable behavior logs and actual security awareness levels. This intermediary enables the system to translate complex psychological states into quantifiable values that can be used for precise user assessment and targeted warning distribution.
2Measurement precision
If psychological characteristics are extracted and analyzed for each user, then security awareness assessment accuracy is improved, but processing time and computational resources increase
Solution Approach 1:
The system performs preliminary extraction of psychological characteristics from questionnaire responses and behavioral characteristics from log data before security assessment is needed. By pre-processing and storing these characteristic values, the system avoids performing complex extraction operations in real-time, thus reducing processing time when security warnings need to be issued while maintaining high assessment accuracy.
3Reliability
If targeted measures are distributed to users based on psychological characteristic values, then security awareness improvement is enhanced, but system complexity for measure distribution increases
Solution Approach 1:
The system applies local quality by distributing different types of security measures tailored to each user's specific psychological characteristic profile. Instead of a uniform warning system, users receive customized measures based on their individual assessment results, which enhances the effectiveness and reliability of security warnings while the modular customization approach prevents excessive overall system complexity.
Data Source
AI summary
A management method comprising, extracting, using a processor, psychological characteristics that are characteristic of people who have experienced a certain incident; extracting, using the processor, behavioral characteristics that are characteristic of people who have experienced a certain incident; obtaining, using the processor, a relational expression between each item of the extracted psychological characteristics and a plurality of items of the extracted behavioral characteristics; and calculating, using the processor, a psychological characteristic value from the relational expression of each psychological characteristic item and a value of log data for each user and, distributing, using the processor, to any user whose psychological characteristic value exceeds a predetermined value, a measure devised for the exceeded psychological characteristic.