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DOI: 10.14569/IJACSA.2023.0140349
PDF

CNN-BiLSTM Hybrid Model for Network Anomaly Detection in Internet of Things

Author 1: Bauyrzhan Omarov
Author 2: Omirlan Auelbekov
Author 3: Azizah Suliman
Author 4: Ainur Zhaxanova

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 14 Issue 3, 2023.

  • Abstract and Keywords
  • How to Cite this Article
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Abstract: Anomaly detection in internet of things network traffic is a critical aspect of intrusion and attack detection, in which a deviation from typical behavior signals the existence of malicious or inadvertent assaults, faults, flaws, and other issues. The necessity to examine a large number of security events to identify anomalous behavior of smart devices adds to the urgency of addressing the challenge of picking machine-learning and deep learning models for identifying anomalies in network traffic. For the challenge of binary data categorization, a software implementation of an intrusion detection system based on supervised-learning algorithms has been completed. The UNSW-NB15 open dataset, which contains 2,540,044 records - vectors of TCP/IP network connection signals and their associated class labels are used to train and test the system. This research compares different machine-learning models and proposes CNN-BiLSTM hybrid model for IoT network intrusion detection. The metrics for measuring the quality of classification and the running duration of algorithms for different ratios of train and test samples are the result of the built framework testing.

Keywords: IoT; internet of things; network anomalies; network security; anomaly attack; machine learning; supervised learning; UNSW-NB15

Bauyrzhan Omarov, Omirlan Auelbekov, Azizah Suliman and Ainur Zhaxanova, “CNN-BiLSTM Hybrid Model for Network Anomaly Detection in Internet of Things” International Journal of Advanced Computer Science and Applications(IJACSA), 14(3), 2023. http://dx.doi.org/10.14569/IJACSA.2023.0140349

@article{Omarov2023,
title = {CNN-BiLSTM Hybrid Model for Network Anomaly Detection in Internet of Things},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2023.0140349},
url = {http://dx.doi.org/10.14569/IJACSA.2023.0140349},
year = {2023},
publisher = {The Science and Information Organization},
volume = {14},
number = {3},
author = {Bauyrzhan Omarov and Omirlan Auelbekov and Azizah Suliman and Ainur Zhaxanova}
}



Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

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