Dynamic Privilege Allocation via Cognitive Context Evaluation
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Solution Overview
Problem
Current access and privilege management systems are vulnerable to unauthorized users who discover valid credentials and legitimate users with malicious intent, as they grant the same access and privileges upon authenticating login credentials without distinguishing between authentic but malicious users and unauthorized users.
Innovation Solution
The Cognitive Device-login Based Multi-level Privilege Management (CDLB-MPM) system dynamically allocates privileges based on a Context Trust Score (CTS) calculated from various attributes of a login request, continuously monitors user activity, and adjusts privileges using machine-learning methods to detect and mitigate potential security risks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If the system grants the same access and privileges to all authenticated users based on predefined roles, then the ease of operation and administrative simplicity is improved, but the security and ability to distinguish between authentic malicious users and unauthorized users deteriorates
Solution Approach 1:
The patent implements dynamic privilege allocation where user permissions are not fixed but adapt in real-time based on contextual factors. The system continuously evaluates login context attributes and user behavior patterns to dynamically adjust privilege levels, transitioning from static RBAC to a dynamic adaptive access control model that responds to changing security conditions
Solution Approach 2:
The system changes multiple parameters simultaneously including login context attributes (device information, location, time), user behavior patterns, and threat level assessments to determine appropriate privilege levels. By monitoring and evaluating multiple varying parameters, the system distinguishes between legitimate users and threats while maintaining administrative simplicity through automated decision-making
2Reliability
If the system implements dynamic privilege allocation based on multiple factors, then the security and ability to detect malicious users is improved, but the device complexity and computational requirements worsen
Solution Approach 1:
The system performs preliminary actions by pre-establishing baseline user behavior patterns, pre-defining contextual attribute weights, and pre-configuring threat response protocols. Machine learning models are trained in advance on historical data to recognize malicious patterns, enabling faster real-time decision-making without excessive computational complexity during active authentication
Solution Approach 2:
The patent introduces intermediary components including a context evaluation module that mediates between raw login data and privilege decisions, and a machine learning classifier that acts as an intermediary between user behavior data and security assessments. These intermediaries simplify the overall system architecture by breaking down complex decision-making into manageable modular components
3Reliability
If the system continuously monitors and evaluates user activity to adjust privileges, then the ability to detect and mitigate malicious activities is improved, but the loss of time and computational overhead worsens
Solution Approach 1:
The system implements periodic evaluation of user activity where privilege reassessment occurs at defined intervals or triggered by specific events rather than continuously. This periodic action includes evaluating user behavior patterns at login, during scheduled checkpoints, and when anomaly thresholds are reached, balancing thorough monitoring with acceptable processing time
Solution Approach 2:
The patent applies skipping by focusing computational resources on critical evaluation points rather than analyzing every single user action in real-time. The system skips routine low-risk operations and rushes through evaluation of high-risk behaviors or anomaly detection events, processing only the most relevant security-critical data points to minimize time loss
Data Source
AI summary
In an embodiment, a method for allocating privileges on a computer network includes calculating a permission level based at least in part on a login context associated with a login request for connection to a network from a user via a client device, adjusting the calculated permission level such that a resulting first adjusted permission level does not exceed a previous permission level assigned to the client device, assigning the first adjusted permission level to the client device, collecting activity data representative of network activity by the user while logged in to the network, and classifying, using a classifier model, at least a portion of the activity data to generate a second adjusted permission level for the client device.