Reinforcement Learning for the Optimization of Adaptive Intrusion Detection Systems

Network intrusion detection is an activity of increasing importance due to the annual rise in attacks.In the literature, the use of machine learning solutions is one of the most common mechanisms for improving the performance of detection systems.One of the main problems with these approaches is data imbalance: the volume of malicious traffic is much lower than normal traffic, making it difficult to create an effective model.The use of ensemble models, which combine several individual models, can increase robustness against data imbalance.To try to improve the effectiveness of ensemble models against imbalance, in this work, we apply reinforcement learning to combine the individual predictions of the ensemble models, with the objective of improving predictions compared to classic weighted voting algorithms.