A hybrid CNN and attentive hierarchical BiLSTM model with SMO for intrusion detection in IIoT

Abstract Many of intrusion detection systems (IDSs) analyses only a portion of packet data of fixed size for intrusion detection in industrial internet of things (IIoT) network, which limits the detection accuracy. In order to ensure higher detection accuracy it is important to design an IDSs that can analyse all features present in the packet. Models based on deep learning (DL) has great ability to process high-dimensional complex data. This study introduces a novel IDS called CNN-AH-BiLSTM that employs spider monkey optimization (SMO) to optimize data which enables system to not only deal with high-dimensional data but also ability to handle uncertainties in the data. SMO reduces feature dimension upto 40% which lowers the computational complexity and memory requirements which makes model efficient for deployment in resource constrained IIoT enviornments. Convolution Neural Network (CNN) is used for robust feature extraction. For classification a hierarchical attentive BiLSTM model is presented which enhances the system’s ability to focus on crucial temporal features. Finally self-attention layer is employed to enhance the model’s focus on critical features. Attention layer assigns weights to important parts of the input sequence. With this model we have tried to solve the problem of low detection accuracy. Performance assessment is done on three different standard datasets namely NSL-KDD, X-IIoTID and Edge-IIoTset datasets, with the accuracy 99.96%, 98.75 and 99.82 for multiclass classification and 99.98%, 98.88% and 99.93% for binary classification respectively. We have validated the proposed approach by not only conducting an extensive evaluation but also comparing the proposed model with various machine learning (ML), deep learning (DL) models as well as with other current related research, which highlight the effectiveness of proposed model.