PDAE: Efficient network intrusion detection in IoT using parallel deep auto-encoders

Network intrusion detection is one of the most important components of mobile networks security. In recent years, the application of neural networks has been very popular in network intrusion detection. However, due to limited resources of IoT devices, fast detection of the intrusion requires a high accuracy neural network with a lightweight and efficient architecture. Therefore, the conventional architectures of neural networks are not suitable for intrusion detection in IoT devices due to the use of a large number of parameters in these models concerning the limited processing resources in IoT devices. This paper presents a new and lightweight architecture based on Parallel Deep Auto-Encoder (PDAE) that uses both locally and surrounding information around individual values in the feature vector. This type of separation of features allows us to increase the accuracy of the model while greatly reducing the number of parameters, memory footprint, and the need for processing power. The effectiveness of the proposed model is evaluated using KDDCup99, CICIDS2017, and UNSW-NB15 datasets and the results shows the superiority of the proposed model over the state-of-the-art algorithms in terms of both accuracy and performance.(c) 2022 Elsevier Inc. All rights reserved.

Automated summary

This paper presents a new and lightweight architecture for intrusion detection in IoT devices based on Parallel Deep Auto-Encoder (PDAE). By separating features using local and surrounding information, the accuracy of the model is improved while reducing the number of parameters and resource requirements. The effectiveness of the proposed model is evaluated and it outperforms state-of-the-art algorithms in terms of both accuracy and performance.