Optimizing cnn-Bigru performance: Mish activation and comparative analysis with Relu

TL;DR

This paper demonstrates that using Mish activation function in CNN-BiGRU models significantly improves intrusion detection performance over ReLU, validated across three datasets, highlighting the importance of activation functions in deep learning.

Contribution

The study introduces Mish activation into CNN-BiGRU models for intrusion detection and provides a comparative analysis showing Mish's superior performance over ReLU.

Findings

Mish outperforms ReLU in intrusion detection accuracy.

Mish enhances neural network capability to model complex patterns.

Performance improvements are consistent across multiple datasets.

Abstract

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.