  Deep learning is currently extensively employed across a range of research
domains. The continuous advancements in deep learning techniques contribute to
solving intricate challenges. Activation functions (AF) are fundamental
components within neural networks, enabling them to capture complex patterns
and relationships in the data. By introducing non-linearities, AF empowers
neural networks to model and adapt to the diverse and nuanced nature of
real-world data, enhancing their ability to make accurate predictions across
various tasks. In the context of intrusion detection, the Mish, a recent AF,
was implemented in the CNN-BiGRU model, using three datasets: ASNM-TUN,
ASNM-CDX, and HOGZILLA. The comparison with Rectified Linear Unit (ReLU), a
widely used AF, revealed that Mish outperforms ReLU, showcasing superior
performance across the evaluated datasets. This study illuminates the
effectiveness of AF in elevating the performance of intrusion detection
systems.
