Version 1
: Received: 12 August 2024 / Approved: 13 August 2024 / Online: 14 August 2024 (07:18:12 CEST)
How to cite:
Xiao, Y.; Feng, Y.; Sakurai, K. An Efficient Detection Mechanism of Network Intrusions in IoT Environments using Autoencoder and Data Partitioning. Preprints2024, 2024080945. https://doi.org/10.20944/preprints202408.0945.v1
Xiao, Y.; Feng, Y.; Sakurai, K. An Efficient Detection Mechanism of Network Intrusions in IoT Environments using Autoencoder and Data Partitioning. Preprints 2024, 2024080945. https://doi.org/10.20944/preprints202408.0945.v1
Xiao, Y.; Feng, Y.; Sakurai, K. An Efficient Detection Mechanism of Network Intrusions in IoT Environments using Autoencoder and Data Partitioning. Preprints2024, 2024080945. https://doi.org/10.20944/preprints202408.0945.v1
APA Style
Xiao, Y., Feng, Y., & Sakurai, K. (2024). An Efficient Detection Mechanism of Network Intrusions in IoT Environments using Autoencoder and Data Partitioning. Preprints. https://doi.org/10.20944/preprints202408.0945.v1
Chicago/Turabian Style
Xiao, Y., Yaokai Feng and Kouichi Sakurai. 2024 "An Efficient Detection Mechanism of Network Intrusions in IoT Environments using Autoencoder and Data Partitioning" Preprints. https://doi.org/10.20944/preprints202408.0945.v1
Abstract
In recent years, with the development of the Internet of Things and distributed computing, the "server-edge device" architecture has been widely deployed. This study focuses on leveraging autoencoder technology to address the binary classification problem in network intrusion detection, aiming to develop a lightweight model suitable for edge devices. Traditional intrusion detection models face two main challenges when directly ported to edge devices: inadequate computational resources to support large-scale models and the need to improve the accuracy of simpler models. To tackle these issues, this research utilizes Extreme Learning Machine for its efficient training speed and compact model size to implement autoencoders. Two improvements over the latest related work are proposed: First, to improve data purity and ultimately enhance detection performance, this study partitions the data into multiple regions based on the prediction results of these autoencoders. Second, leveraging autoencoder characteristics to investigate further the data within each region. We used the public dataset NSL-KDD to test the behavior of the proposed mechanism. The experimental results show that when dealing with multi-class attacks, the model's performance was significantly improved, the accuracy and the F1-score are improved by 3.5% and 2.9%, respectively maintaining its lightweight nature.
Keywords
autoencoder; network intrusion detection; model accuracy improvement; Extreme Learning Machine
Subject
Computer Science and Mathematics, Computer Networks and Communications
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
