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

IEEE Internet of Things Journal, vol. 9, pp. 9960–9972

Abstract

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 article, we propose a novel NID method for IoT based on the 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 principal component analysis (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 multiclassification task, we adopt the NID loss that acts as a better loss function to replace the standard cross-entropy loss for dealing with the problem of uneven distribution of samples. The results of experiments on two real-world NID data sets 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.

Authors 7

1. Ruijie Zhao

   Shanghai Jiao Tong University

   Affiliation as printed

   School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, China

2. Guan Gui

   Nanjing University of Posts and Telecommunications

   Affiliation as printed

   College of Telecommunications and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing, China

3. Zhi Xue

   Shanghai Jiao Tong University

   Affiliation as printed

   School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, China

4. Jie Yin

   Nanjing University · Jiangsu Police Officer College

   Affiliation as printed

   Jiangsu Provincial Public Security Department Key Laboratory of Digital Forensics, Department of Network Security Corps, Jiangsu Electronic Data Forensics and Analysis Engineering Research Center, Jiangsu Police Institute, Nanjing, China

   State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, China

5. Tomoaki Otsuki Ohtsuki

   Keio University

   Affiliation as printed

   Department of Information and Computer Science, Keio University, Tokyo, Japan

6. Bamidele Adebisi

   Manchester Metropolitan University

   Affiliation as printed

   Department of Engineering, Faculty of Science and Engineering, Manchester Metropolitan University, Manchester, U.K

7. Haris Gacanin Aachen Institute for Communication Technologies and Embedded Systems

   RWTH Aachen University

   Affiliation as printed

   Institute for Communication Technologies and Embedded Systems, RWTH Aachen University, Aachen, Germany