Intrusion detection for capsule networks based on dual routing mechanism

The combination of deep learning and intrusion detection has become a hot topic in today's information security. In today's risky network environment, the ability to accurately detect anomalous data is an important task for intrusion detection. In an intrusion detection system, each piece of data contains multiple features. However, not every feature will determine the nature of the data, on the contrary, too many features will affect the model's judgment. In this paper, we propose an intrusion detection model of a deep capsule network based on an attention mechanism. The model uses a deep capsule network to enhance the extraction of features, and the attention mechanism is carried out to make the model focus on the features with large influences. The features are captured in multiple directions by a double routing algorithm and two strategies are adopted to stabilize the dynamic routing process. Finally, experiments are conducted on the intrusion detection dataset with good results.

Automated summary

This study proposes an intrusion detection model based on deep capsule network and attention mechanism, which can effectively extract key features and accurately detect anomalous data, yielding good experimental results.