Mobile Network Maintenance Cleanup for QoS Manipulation Detection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Mobile networks are vulnerable to data poisoning attacks that compromise the quality of service (QoS) due to automated maintenance processes, leading to unnecessary maintenance actions and degradation in service quality.
Innovation Solution
A method for managing maintenance processes in mobile networks by a centralized entity that receives control-level and user-level reports to detect manipulation, using AI and ML algorithms to identify potential manipulation conditions and trigger a cleanup routine.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated maintenance processes are implemented in mobile networks, then network operation efficiency is improved, but vulnerability to data poisoning attacks increases
Solution Approach 1:
The patent implements a feedback mechanism where the centralized entity receives QoS reports from distributed entities, analyzes them for manipulation conditions, and triggers cleanup routines when attacks are detected. This closed-loop feedback system enables the network to automatically respond to data poisoning attacks while maintaining automated maintenance operations, thus resolving the contradiction between efficiency and reliability.
Solution Approach 2:
The centralized entity acts as an intermediary between distributed entities and the network core, mediating the detection and response to data poisoning attacks. It receives control-level reports from distributed entities, analyzes them for manipulation, and coordinates cleanup actions, thereby protecting the automated maintenance system from attacks without compromising operational efficiency.
2Ease of operation
If distributed entities operate autonomous maintenance processes, then local QoS optimization is improved, but detection of manipulation becomes more difficult
Solution Approach 1:
The system implements feedback by having distributed entities send control-level QoS reports to the centralized entity, which analyzes these reports for manipulation conditions. This upward feedback loop enables centralized detection of attacks while distributed entities continue their autonomous maintenance operations, resolving the contradiction between local optimization and attack detection.
Solution Approach 2:
The patent adds a new dimension to manipulation detection by analyzing control-level reports from distributed entities at the centralized level. Instead of relying solely on local detection at distributed entities, the system elevates the detection function to the centralized entity, creating a multi-layered detection architecture that maintains local autonomy while enabling centralized oversight.
3Reliability
If cleanup routines are triggered frequently to remove manipulations, then QoS consistency is improved, but network signaling overhead increases
Solution Approach 1:
The system applies partial action by triggering cleanup routines selectively based on detected manipulation conditions rather than continuously. The centralized entity analyzes control-level reports and only initiates cleanup actions when data poisoning attacks are detected, avoiding unnecessary signaling overhead while maintaining QoS consistency when needed.
Solution Approach 2:
The feedback mechanism enables the system to trigger cleanup routines only when manipulation conditions are detected in control-level reports. This conditional response based on feedback information ensures QoS consistency is maintained when attacks occur while avoiding unnecessary signaling overhead during normal operations.
Data Source
AI summary
Approaches described herein relate to a method of managing a maintenance process in a mobile network comprising, by a centralized entity of the mobile network, receiving from a first distributed entity configured for maximizing its QoS by operating the maintenance process a QoS-related control-level report; based on the control-level report, determining a first QoS status of the first distributed entity; in response to a mobile device handed over from the first to a second distributed entity: based on a user-level report related to the first distributed entity's QoS and received from the mobile device, determining a second QoS status of the first distributed entity; if the second QoS status indicates a QoS issue, identifying a manipulation condition of the maintenance process based on the first QoS status, and if the manipulation condition indicates a potential manipulation of the maintenance process, triggering a cleanup routine for removing the potential manipulation.