A Novel Intrusion Detection Method Based on Lightweight Neural Network for Internet of Things

Ruijie Zhao, Guan Gui, Zhi Xue, Jie Yin, Tomoaki Ohtsuki, Bamidele Adebisi, Haris Gacanin

Research output: Contribution to journalArticlepeer-review


The purpose of a network intrusion detection (NID) is to detect intrusions in the network, which plays a critical role in ensuring the security of the Internet of Things (IoT). Recently, deep learning (DL) has achieved a great success in the field of intrusion detection. However, the limited computing capabilities and storage of IoT devices hinder the actual deployment of DL-based high-complexity models. In this paper, we propose a novel NID method for IoT based on lightweight deep neural network (LNN). In the data preprocessing stage, to avoid high-dimensional raw traffic features leading to high model complexity, we use the PCA algorithm to achieve feature dimensionality reduction. Besides, our classifier uses the expansion and compression structure, the inverse residual structure, and the channel shuffle operation to achieve effective feature extraction with low computational cost. For the multi-classification task, we adopt NID Loss that acts as a better loss function to replace standard cross-entropy loss for dealing with the problem of uneven distribution of samples. The results of experiments on two real-world NID datasets demonstrate that our method has excellent classification performance with low model complexity and small model size, and it is suitable for classifying the IoT traffic of normal and attack scenarios.

Original languageEnglish
JournalIEEE Internet of Things Journal
Publication statusAccepted/In press - 2021


  • Computational modeling
  • deep learning
  • Deep learning
  • Dimensionality reduction
  • Feature extraction
  • Internet of Things
  • Internet of things
  • intrusion detection
  • lightweight neural network.
  • Principal component analysis
  • Support vector machines

ASJC Scopus subject areas

  • Signal Processing
  • Information Systems
  • Hardware and Architecture
  • Computer Science Applications
  • Computer Networks and Communications


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