An Efficient Intrusion Detection Method Based on Dynamic Autoencoder

The proliferation of wireless sensor networks (WSNs) and their applications has attracted remarkable growth in unsolicited intrusions and security threats, which disrupt the normal operations of the WSNs. Deep learning (DL)-based network intrusion detection (NID) methods have been widely investigated and developed. However, the high computational complexity of DL seriously hinders the actual deployment of the DL-based model, particularly in the devices of WSNs that do not have powerful processing performance due to power limitation. In this letter, we propose a lightweight dynamic autoencoder network (LDAN) method for NID, which realizes efficient feature extraction through lightweight structure design. Experimental results show that our proposed model achieves high accuracy and robustness while greatly reducing computational cost and model size.

Automated summary

The rapid growth of wireless sensor networks and applications has led to an increase in unsolicited intrusions and security threats, disrupting normal operations. Deep learning-based network intrusion detection methods have been widely studied, but their high computational complexity hinders deployment in devices with limited processing power. This letter introduces a lightweight dynamic autoencoder network (LDAN) for NID, which efficiently extracts features through a lightweight structure design. Experimental results demonstrate that the proposed model achieves high accuracy and robustness while significantly reducing computational cost and model size.