IoT Intrusion Detection System Based on LSTM Model

DOI

10.2991/978-94-6463-040-4_209How to use a DOI?

Keywords

LSTM; CTU-13; CICIDS-2017; TLS; CNN; Accuracy

Abstract

Aiming at the problems of time-consuming feature extraction and general efficiency in the detection of en-crypted traffic by traditional machine learning algorithms, an intrusion detection model based on deep learning long short-term memory network (LSTM) was proposed. First, the malicious encrypted traffic in the CTU-13 data set and the normal traffic in the CICIDS-2017 data set are extracted to form a data set; then the binary classification data set processing is completed based on the secure transport layer protocol; finally, the LSTM and one-dimensional convolutional neural networks are trained. Network, two-dimensional convolutional neural network and convolutional neural network-long short-term memory network four deep learning models. The experimental results show that LSTM has significant advantages over the other three models in five evaluation parameters, the accuracy of key parameters is as high as 99.84%, and it performs well in terms of CPU and memory usage, which meets the security requirements of the Internet of Things.

Copyright

© 2023 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Weiqun Li
AU  - Chaowen Chang
PY  - 2022
DA  - 2022/12/27
TI  - IoT Intrusion Detection System Based on LSTM Model
BT  - Proceedings of the 2022 3rd International Conference on Artificial Intelligence and Education (IC-ICAIE 2022)
PB  - Atlantis Press
SP  - 1404
EP  - 1409
SN  - 2589-4900
UR  - https://doi.org/10.2991/978-94-6463-040-4_209
DO  - 10.2991/978-94-6463-040-4_209
ID  - Li2022
ER  -