Enhancing intrusion detection in next-generation networks based on a multi-agent game-theoretic framework

Abstrak

With cyber threats becoming increasingly sophisticated, existing intrusion detection systems (IDS) in next generation networks (NGNs) are subjected to more false-positives and struggles to offer robust security feature, highlighting a critical need for more adaptive and reliable threat detection mechanisms. This research introduces a novel IDS that leverages a dueling deep Q-network (DQN) a reinforcement learning algorithm within game-theoretic framework simulating a multi-agent adversarial learning scenario to address these challenges. By employing a customized OpenAI Gym environment for realistic threat simulation and advanced dueling DQN mechanisms for reduced overestimation bias, the proposed scheme significantly enhances the adaptability and accuracy of intrusion detection. Comparative analysis against current state-of-the-art methods reveals that the proposed system achieves superior performance, with accuracy and F1-score improvements to 95.02% and 94.68%, respectively. These results highlight the potential scope of the proposed adaptive IDS to provide a robust defense against the dynamic threat landscape in NGNs.