On Detection of Manipulative Cognitive Functions in Cognitive Autonomous Networks
Anubhab Banerjee, Stephen S. Mwanje, Georg Carle · 2021
Introduction of artificial intelligence is expected to raise the degree of automation in mobile networks by succeeding Self Organizing Networks (SON) with Cognitive Autonomous Networks (CAN). In CAN, learning based Cognitive Functions (CFs) work on different network parameters to optimize specific Key Performance Indicators (KPIs). However, learning ability of a CF poses a serious threat to the stability of the system. A manipulative CF (like a rogue agent) may use its learning capabilities to understand the working procedure of the system and manipulate it to achieve its own objective. Existence of such a CF can cause severe performance degradation of the overall system. In this paper we propose a simple yet effective machine learning based approach to detect manipulative CF(s) in CAN. We evaluate the performance of our proposed solution in a simulation environment that closely resembles a real life 5G scenario and provide analysis of the results with necessary precautions to be taken in a multi vendor scenario.