Intrusion Detection Model of Internet of Things Based on Deep Learning

Yan Wang1, Dezhi Han1 and Mingming Cui1

  1. College of Information Engineering
    Shanghai Maritime University , China, dzhan, Mingming


The proliferation of Internet of Things (IoTs) technology is being seriously impeded by insecure networks and data. An effective intrusion detection model is essential for safeguarding the network and data security of IoTs. In this paper, a hybrid parallel intrusion detection model based on deep learning (DL) called HPIDM features a three-layer parallel neural network structure. Combining stacked Long short-term memory (LSTM) neural networks with convolutional neural network (CNN) and SK Net self-attentive mechanism in the model allows HPIDM to learn temporal and spatial features of traffic data effectively. HPIDM fuses the acquired temporal and spatial feature data and then feeds it into the CosMargin classifier for classification detection to reduce the impact of data imbalance on the performance of the Intrusion Detection System (IDS). Finally, HPIDM was experimentally compared with classical intrusion detection models and the two comparative models designed in this paper, and the experimental results show that HPIDM achieves 99.87% accuracy on the ISCX-IDS 2012 dataset and 99.94% accuracy on the CICIDS 2017 dataset. In addition, it outperforms other comparable models in terms of recall, precision, false alarm rate (FAR), and F1 score, showing its feasibility and superiority.

Key words

intrusion detection; deep learning(DL); Long short-term memory (LSTM); convolutional neural network (CNN); SK Net self-attentive mechanism

Digital Object Identifier (DOI)

Publication information

Volume 20, Issue 4 (September 2023)
Year of Publication: 2023
ISSN: 2406-1018 (Online)
Publisher: ComSIS Consortium

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How to cite

Wang, Y., Han, D., Cui, M.: Intrusion Detection Model of Internet of Things Based on Deep Learning. Computer Science and Information Systems, Vol. 20, No. 4. (2023),