Journal Article

A network intrusion detection system based on deep learning in the IoT

Yang, Guang, Dai, Lie, Wang, Xiao

2024

Overview

As industrial and everyday devices become increasingly interconnected, the data volume within the Internet of Things (IoT) has experienced a substantial surge. This surge in data presents a heightened risk of IoT environments being vulnerable to cyber attacks, which poses a significant threat to the seamless functioning of both industrial and daily activities. Therefore, the implementation of Network Intrusion Detection System (IDS) is vital for safeguarding the security of IoT network environments. This paper introduces a network intrusion detection model based on deep learning (DL). The model aims to enhance detection accuracy by extracting features from both the spatial and temporal dimensions of network traffic data. To tackle the challenge of low detection accuracy arising from data imbalance, in this study, a Conditional Tabular Generative Adversarial Network (CTGAN) is utilized to generate synthetic data for the minority class. The objective is to enhance the volume of minority class samples, address data imbalance, and subsequently enhance the accuracy of network intrusion detection. The classification performance of the proposed model is validated on UNSW-NB15, CIC-IDS2018, and CIC-IOT2023 datasets. The experimental findings demonstrate that the suggested model attains elevated levels of classification accuracy across all three datasets. The model presented in this article is particularly well suited to handle multi-class intrusion detection tasks. The model demonstrates superior performance compared to other models used for comparison.

• Publisher: Springer US,Springer Nature B.V
• Subject: Access control / Accuracy / Behavior / Classification / Communications traffic / Compilers / Computer Science / Contemporary problems / Cybersecurity / Datasets / Decision trees / Deep learning / Efficiency / Flexibility / Generative adversarial networks / Internet of Things / Interpreters / Intrusion detection systems / Machine learning / Methods / Neural networks / Processor Architectures / Programming Languages / Support vector machines / Synthetic data / Time series