MG Experimental Evaluation of Adaptive Multi-Agent AI Models for Detecting Stealthy Cyber Attacks in Dynamic Network Environments based on MADDPG MG
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Abstract
Cybersecurity faces increasing challenges due to sophisticated and covert attacks targeting dynamic networks. This research aims to explore the effectiveness of adaptive multi-agent AI models in detecting these attacks using an algorithm MADDPG (Multi-Agent Deep Deterministic Policy Gradient) a dynamic network environment was designed to simulate natural data traffic and covert cyberattacks, where each agent monitors a specific part of the network and makes immediate decisions to detect or mitigate attacks .
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