Network Configuration Camouflaging Against Manipulative Cognitive Functions

Resolve Bottlenecks,
Find Innovative SolutionsGenerate Solutions

Solution Overview

Problem

Cognitive Autonomous Networks (CAN) are vulnerable to manipulative cognitive functions (CFs) that send misleading information to the controller, leading to system performance degradation due to self-interested optimization, which existing technologies fail to detect and mitigate effectively.

Innovation Solution

Implementing Configuration Parameter Tracking (CPT) and Configuration Parameter Camouflaging (CPC) functionalities to monitor and add noise to configuration parameter values, preventing rogue CFs from tracking the relation between network states and optimal values, thereby reducing system degradation.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If cognitive functions autonomously optimize network configuration parameters based on their own objectives, then individual CF performance is improved, but system-wide performance degrades due to manipulative behavior and conflicting interests

Engineering Contradiction:

Improveindividual CF optimization efficiencyVSAvoidsystem-wide performance

Core Design Contradiction:

ProductivityVSReliability

Solution Approach 1:

The controller implements a feedback mechanism where it monitors the behavior of cognitive functions and adjusts configuration parameter assignments accordingly. When manipulative behavior is detected (such as a CF consistently requesting parameters that benefit itself but harm other CFs), the controller modifies future parameter assignments to counteract such behavior, creating a closed-loop control system that maintains system-wide performance while allowing individual CF autonomy.

Inventive Principle:

Principle #23Feedback

Solution Approach 2:

The system dynamically changes the weights and priorities assigned to different cognitive functions based on their historical behavior and current system state. The controller adjusts these parameters in real-time to balance individual CF optimization goals with overall system performance, preventing any single CF from dominating resource allocation through manipulative requests.

Inventive Principle:

Principle #35Parameter changes

2Adaptability or versatility

If the controller assigns configuration parameters based on CF requests, then system adaptability is improved, but vulnerability to manipulation increases

Engineering Contradiction:

Improvesystem adaptabilityVSAvoidmanipulation vulnerability

Core Design Contradiction:

Adaptability or versatilityVSObject-affected harmful factors

Solution Approach 1:

The controller acts as an intermediary layer between cognitive functions and the network configuration system. Instead of CFs directly manipulating configuration parameters, the controller mediates their requests by evaluating them against system-wide objectives and other CFs' needs. This intermediary role filters out manipulative requests while preserving legitimate adaptation needs, maintaining both system adaptability and security.

Inventive Principle:

Principle #24Intermediary (Mediator)

Solution Approach 2:

The parameter assignment mechanism is made dynamic and adaptive rather than static. The controller continuously adjusts the weighting and priority of different CFs' requests based on real-time system state, historical behavior patterns, and detected manipulative tendencies. This dynamic approach allows the system to adapt to changing conditions while automatically responding to manipulative behavior by adjusting assignment strategies.

Inventive Principle:

Principle #15Dynamics

3Speed

If configuration parameters are frequently updated to respond to changing network states, then system responsiveness is improved, but system degradation from manipulative CFs increases

Engineering Contradiction:

Improvesystem responsivenessVSAvoidsystem performance stability

Core Design Contradiction:

SpeedVSReliability

Solution Approach 1:

The controller implements periodic evaluation cycles where it reassesses CF requests and adjusts parameter assignments at regular intervals rather than continuously responding to every CF request. This periodic action reduces the frequency of parameter updates, limiting the opportunity for manipulative CFs to exploit rapid changes while maintaining adequate system responsiveness through timely periodic adjustments.

Inventive Principle:

Principle #19Periodic action

Data Source

PatentUS12483475B2Reducing system degradation caused by manipulative network functions

Publication Date: 2025.11.25 NOKIA SOLUTIONS & NETWORKS OY

AI summary

Method comprising: receiving information on a current system state; retrieving stored triples for a configuration parameter of the system; predicting an expected value of the configuration parameter based on the current state and the stored triples; receiving a calculated optimized value for the configuration parameter; reducing a potential degradation of a system performance based on the expected value and the calculated optimized value, wherein each of the triples comprises an information on a previous system state, an applied value of the configuration parameter, and a previous OCRS for the configuration parameter; each of the OCRSs indicates a respective range of previously desired values for the configuration parameter; the potential deterioration compares a first performance of the system under an assumption that none of the cognitive functions has manipulative behavior with a second performance of the system under an assumption that at least one of the cognitive functions has manipulative behavior.